Abstract:
A method of fusing sensor detection probabilities. The fusing of detection probabilities may allow a first force to detect an imminent threat from a second force, with enough time to counter the threat. The detection probabilities may include accuracy probability of one or more sensors and an available time probability of the one or more sensors. The detection probabilities allow a determination of accuracy of intelligence gathered by each of the sensors. Also, the detection probabilities allow a determination of a probable benefit of an additional platform, sensor, or processing method. The detection probabilities allow a system or mission analyst to quickly decompose a problem space and build a detailed analysis of a scenario under different conditions including technology and environmental factors.

Description:
RELATED APPLICATIONS 
       [0001]    This application claims the benefit of U.S. Provisional Application No. 61/929,250 filed Jan. 20, 2014, which is hereby incorporated herein by reference. 
         [0002]    Also, this application is related to two commonly-assigned concurrently-filed applications, “Integrated Digital Weapons Factory and Digital Operations Center for Producing, Deploying, Assessing, and Managing Digital Defects” (Attorney Docket No. RAYTP0650USA), which is hereby incorporated herein by reference in its entirety; and “System and Method for Asymmetric Missile Defense” (Attorney Docket No. RAYTP0653USA), which is hereby incorporated herein by reference in its entirety. 
     
    
     BACKGROUND 
       [0003]    Sensor platforms may include a plurality of sensors to detect various objects or events in a local or wide geographic area. For example, a drone flying over a land mass may be equipped with an infrared sensor and a camera to detect objects that are visible, as well as heat signatures of objects that may be covered. The drone may provide the detections to a command station that can analyze the information and determine a course of action. 
         [0004]    Presently, multiple sensor platforms may be utilized at any given moment, but the range and availability of these platforms is limited. The limited range and availability of the sensors prevents detection of some objects or events that would otherwise be desirable to observe, and can lead to providing incomplete information to a war fighter, as well as providing information at a time that is too late for the war fighter to act on. 
       SUMMARY OF INVENTION 
       [0005]    The present invention provides a method of fusing sensor detection probabilities. The fusing of detection probabilities may allow a first force to detect an imminent threat from a second force, with enough time to counter the threat. The detection probabilities may include accuracy probability of one or more sensors and an available time probability of the one or more sensors. The detection probabilities allow a determination of accuracy of intelligence gathered by each of the sensors. Also, the detection probabilities allow a determination of a probable benefit of an additional platform, sensor, or processing method. 
         [0006]    The detection probabilities allow a system or mission analyst to quickly decompose a problem space and build a detailed analysis of a scenario under different conditions including technology and environmental factors. The detection probabilities may include a probability of detecting, locating, and/or tracking an object, and allow a determination of a current effectiveness and/or allow a determination of an expected effectiveness of additional and/or fewer resources. 
         [0007]    Knowing an expected effectiveness of additional resources, such as an additional platform, sensor, and/or processing method, supports automated decision aides and may allow a determination to deploy such resource based on the expected effectiveness or benefit of meeting a threshold level. Similarly, knowing an expected effectiveness of fewer resources, such as a removed platform, sensor, and/or processing method, supports automated decision aides and may allow a determination to remove such resource based on the expected effectiveness or loss. The automated decision aides may include a computer-based information system that supports decision making activities based on an assessed effectiveness. 
         [0008]    Assessing effectiveness based on adding or removing a resource allows determination of a most effective combination of resources to monitor a single object or multiple objects across various locations and time. Models demonstrating benefits of fusing data from multiple data types and platforms may be created to demonstrate such effectiveness. The effectiveness may be determined by fusing (e.g., integrating) multi-source and/or multi-INT products for a plurality of platforms, sensors, and/or target objects. Also, the determination of effectiveness may be platform, sensor, or intelligence independent, and may incorporate intelligence level parameters to provide an effectiveness that is tailored for a specific object, platform, sensor, environmental condition, and/or timeline scenario. 
         [0009]    Also, the method of fusing sensor detection probabilities may allow a determination of statistical intelligence surveillance and reconnaissance (ISR) capabilities, effectiveness, gaps, as well as analysis of effects of adding or removing capabilities, a technology, and/or tactic. 
         [0010]    Additionally, the method of fusing sensor detection probabilities may allow a determination of a minimum threshold of a performance parameter for new systems to provide a requisite benefit to resulting intelligence surveillance and reconnaissance products and/or situational awareness. 
         [0011]    The method of fusing sensor detection probabilities may be automated and performed by a computer system, for example a computer in data communication with a plurality of platforms and corresponding sensors. Alternatively, a system of computers and computer networks may communicate between one another to repeatedly perform portions of the method. In an embodiment, a computer or computer network directs a platform or sensor to a location and/or determines a target for the platform and/or sensor to focus on. 
         [0012]    An aspect of the invention determines an effectiveness of a sensor platform. 
         [0013]    Another aspect of the invention determines a location of a target, based on a determination of effectiveness of detection of the target object. 
         [0014]    Yet another aspect of the invention determines an intent of a target object, based on a determination of one or more locations of the target object. The determination of intent may include a determination of a damage capability of the target object. 
         [0015]    According to one aspect of the invention, a method of determining sensor detection probabilities of a system of platforms each having one or more respective sensors, the method comprising determining a set of detection parameters of one or more combinations of platforms and respective sensors of the system of platforms based on a target object, environmental conditions affecting detection of the target object during a first point in time of a timeline of the target object, and capabilities of each of the one or more combinations, deriving a time limit to detect the target object, the time limit being at a second point in time after the first point in time, and based on a threat level of the target object, updating the set of detection parameters based on the second point in time, deriving a time to process a detection of the target object by each of the one or more combinations, deriving an accuracy of the of each of the one or more combinations based on the target object, environmental conditions affecting detection of the target object during the first point in time, and capabilities of each of the one or more combinations, and determining sensor detection probabilities of each sensor of the one or more combinations based on the second point in time, the time to process the detection of the target object, and the accuracy of each of the one or more combinations. Any of the above aspects may include any of the below features individually or in combination. 
         [0016]    The method of determining sensor detection probabilities may further comprise determining that one or more additional combinations of platforms and/or respective sensors should be added to a region based on the sensor detection probabilities. 
         [0017]    The method of determining sensor detection probabilities may further comprise relocating the one or more additional combinations to detect the target object. 
         [0018]    The method of determining sensor detection probabilities may further comprise deriving a time for detection of the target object based on a derived number of detections of each intelligence type of the one or more combinations and a plurality of other combinations, and/or based on a derived time to report result of each intelligence type of the one or more combinations and the plurality of other combinations. 
         [0019]    The method of determining sensor detection probabilities may further comprise deriving a quality of detection based on a derived number of detections of each intelligence type of the one or more combinations and a plurality of other combinations, and/or based on a derived quality of an intelligence product of each intelligence type of the one or more combinations and the plurality of other combinations. 
         [0020]    The capabilities of each of the one or more combinations may be based on historical data of the one or more combinations and subject matter expert data related to the one or more combinations. 
         [0021]    The method of determining sensor detection probabilities may further comprise a method of fixing one or more locations of the target object based on the sensor detection probabilities, and/or a method of tracking based on the sensor detection probabilities and the one or more locations of the target object. 
         [0022]    A computer network including a plurality of computers in electronic communication with one another, wherein at least one of the plurality of computers performs each step of the method of determining sensor detection probabilities, and wherein the computer network may be in electronic communication with the system of platforms and may instruct the system of platforms to add at least one additional combination of a platform and a sensor of the system of platforms to a region, and wherein the system of platforms may relocate the at least one additional combination to the region. 
         [0023]    The method of determining sensor detection probabilities may further comprise determining a probability of one or more of the platforms and the respective sensors will be available to detect an enemy observable based on a weighted average time. 
         [0024]    The method of determining sensor detection probabilities may further comprise determining whether one or more additional platforms and respective sensors of the system of platforms would have a higher probability of detecting the target object based on the time to process the detection of the target object, and/or an accuracy of each of the additional platforms and respective sensors. 
         [0025]    Determining whether the one or more additional platforms and respective sensors would have a higher probability of detecting the target object may be further based on an intelligence method associated with the target object. 
         [0026]    Determining whether the one or more additional platforms and respective sensors would have a higher probability of detecting the target object may be further based on fusion parameters of the one or more additional platforms and respective sensors, and/or based on fusion parameters of the one or more combinations. 
         [0027]    The fusion parameters may include manual fusion parameters. 
         [0028]    The fusion parameters may include automatic fusion parameters. 
         [0029]    The fusion parameters may include a combination of manual and automatic fusion parameters. 
         [0030]    The method of determining sensor detection probabilities may further comprise deriving a probability of detection of the target object based on the target object and/or an intelligence type of the one or more combinations. 
         [0031]    Deriving a probability may be further based on a plurality of target objects, an intelligence type of the one or more combinations, a plurality of other combinations of platforms and sensors of the system of platforms, and/or based on quality metric. 
         [0032]    The target object may include a plurality of enemy vehicles. 
         [0033]    The target object may include a plurality of enemy weapons. 
         [0034]    One or more of the plurality of enemy weapons may be a missile. 
         [0035]    A plurality of the one or more combinations may be relocated from a low threat level area to a high threat level area based on physical conditions including weather, temperature, time, and/or environment. 
         [0036]    According to another aspect of the invention, a method of determining sensor detection probabilities of a system of platforms each having one or more respective sensors, the method comprising identifying intelligence methods of a plurality of combinations of platforms and respective sensors of the system of platforms based on a plurality of target objects, deriving probability parameters of each of the plurality of combinations, mapping accuracy and timeliness parameters of each of the plurality of combinations to the probability parameters, integrating fusion parameters of each of the plurality of combinations with the probability parameters, the accuracy parameters, and the timeliness parameters, deriving a probability for each of the plurality of combinations detecting each target object of the plurality of target objects based on the integrated fusion parameters, deriving tipped probabilities based on the probabilities of each of the plurality of combinations detecting each target object. The above aspect may include any of the above features individually or in combination. 
         [0037]    The foregoing and other features of the invention are hereinafter described in greater detail with reference to the accompanying drawings. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0038]      FIG. 1  is an exemplary mapping of probabilities to an intelligence method and inputting the probabilities into an exemplary Monte Carlo simulation system to determine a probability of a successful intelligence surveillance and reconnaissance tip and/or cue. 
           [0039]      FIG. 2  is a flow chart of an exemplary method of improving detection probabilities. 
           [0040]      FIG. 3  is an exemplary timeline of events of  FIG. 2 . 
           [0041]      FIG. 4  is an exemplary association of each event of  FIG. 3  with desired intelligence and/or comments, assumptions and/or questions. 
           [0042]      FIG. 5  is a schematic representation of identifying intelligence methods of the method of  FIG. 2 , including an exemplary identification of intelligence methods including mapping of the identified intelligence methods to a target object during each event of  FIG. 3 . 
           [0043]      FIG. 6  is an exemplary mapping of platform and/or sensor capabilities to an intelligence method of the intelligence methods of  FIG. 3 . 
           [0044]      FIG. 7  is an exemplary automatic association of a plurality of exemplar sensors to a corresponding platform. 
           [0045]      FIG. 8  is a schematic representation of researching ISR methods of the method of  FIG. 2 , including an exemplary association of platform-specific detection parameters for exemplary environmental conditions. 
           [0046]      FIG. 9  is a schematic representation of adding accuracy and/or timeliness parameters, including an exemplary mapping of a plurality of sensors of  FIG. 6  with time to process and report, and with an average accuracy range of each sensor. 
           [0047]      FIG. 10  is a schematic representation of integrating fusion and/or track parameters, including an exemplary fusion parameters for an exemplary pairing of intelligence. 
           [0048]      FIG. 11  is a schematic representation of deriving the probability of detection, including an exemplary set of environmental conditions, method of selecting intelligence methods, loading of corresponding detection parameters of  FIG. 8 , and a probability of tasking and availability of  FIG. 6 . 
           [0049]      FIG. 12  is the schematic representation of deriving the probability of detection, including an exemplary mapping of a best case probability for a plurality of associated platforms and sensors of  FIG. 6 . 
           [0050]      FIG. 13  is a schematic representation of analyzing results, including a flow chart of an exemplary method of finding, an exemplary method of fixing, and an exemplary method of tracking the target object of  FIG. 5 . 
           [0051]      FIG. 14  is a detailed flow chart of the methods of  FIG. 13 . 
           [0052]      FIG. 15  is a flow chart of the method of finding of  FIG. 13 . 
           [0053]      FIG. 16  is a flow chart of a single observable detection portion of the method of finding of  FIG. 13 . 
           [0054]      FIG. 17  is a flow chart of a single intelligence detection portion of the method of finding of  FIG. 13 . 
           [0055]      FIG. 18  is a flow chart of a group of intelligence detection analysis blocks to support the method of finding of  FIG. 13 . 
           [0056]      FIG. 19  is a flow chart of the method of fixing of  FIG. 13 . 
           [0057]      FIG. 20  is a flow chart of the method of tracking of  FIG. 13 . 
       
    
    
     DETAILED DESCRIPTION 
       [0058]    The principles of this present application have particular application to determining probabilities related to detection of target objects, such as enemy vehicles and/or events, with platforms equipped with sensors, such as vehicles equipped with sensors, and thus will be described below chiefly in this context. It will of course be appreciated, and also understood, that principles of this invention may be applicable to other target objects, such as people, structures, geographic regions, and/or natural phenomena, and to other platforms and sensors. 
         [0059]      FIG. 1  is an exemplary mapping of probabilities  30  to an intelligence method  32  and inputting the probabilities into an exemplary Monte Carlo simulation system  34  to determine a probability of a successful intelligence surveillance and reconnaissance (ISR) tip probability  36  (e.g., tip and/or cue). The probabilities may be adjusted based on a timeline of scenario events, for example an observables module  40  may determine a probability that an observable occurs (P OCC )  42  may change from 0.04 to 0.4 (i.e., 4%-40%) based on surrounding conditions, such a ship communicating at sea. The P OCC    42  may be mapped to the intelligence method  32 . 
         [0060]    A sensor availability and tasking module  44  may determine a probability of time on station (P AV )  46  and/or a probability of time a sensor platform  50  (e.g., an aircraft) is tasked (P TAS )  52  (e.g., a percentage of time each day spent covering a region of the earth). 
         [0061]    A detection and processing module  54  may determine a probability of sensor and system to detect based on observable and data characteristics (P SEN )  56  and/or a probability of delivering on time (P TIM ). 
         [0062]    Each of the probabilities  42 ,  46 ,  52 ,  56 ,  58  may be mapped to the intelligence method  32  and then input into the Monte Carlo simulation system  34 , which may determine a percentage of successful tips over time for each simulation run  70 . The probability of tipping (e.g., successful ISR tips/cues) and a probability of not tipping  72  (e.g., unsuccessful ISR tips/cues) may be based on a current simulation run  74 . 
         [0063]      FIG. 2  is a flow chart of an exemplary method of improving detection probabilities  100 . The method of improving detection probabilities may include decomposing events and steps  102  of a timeline of events  104 , determining desired intelligence  106 , identifying intelligence methods  108 , identifying platforms and sensors  110  from a plurality of platforms and sensors  120 , researching ISR methods  122 , incorporating accuracy and/or timeliness parameters  124 , integrating fusion and/or track parameters  126 , deriving a probability of detection  128 , analyzing the probability of detection  140 , identifying gaps  142 , identifying and/or analyzing a mitigation  144 , and/or mitigating one or more identified gaps  146 . Each of the above steps, and an interrelation between them, is explained below in greater detail. 
         [0064]      FIG. 3  is an exemplary timeline of events  104  of  FIG. 2  that may be decomposed  102  into smaller steps. Each timeline event  200  and target object  202  may be determined and input into the timeline of events  104 . Each timeline event may comprise activities related to one or more forces (e.g., Red, Blue, and/or other). Sub-events may relate to a single force (e.g., Blue) to more clearly separate multi-force capabilities. Alternatively, an appropriate mix of forces may be chosen to support further analysis. At a later time, a given event may be sub-divided into smaller increments to facilitate analysis. Additional timeline events and/or target objects may also be added at a later time. 
         [0065]      FIG. 4  is an exemplary association of each event  200  of  FIG. 3  with desired intelligence  210  and/or comments, assumptions, and/or questions  212  to determine desired intelligence  106 . The determination of desired intelligence  106  may include determining a required intelligence for a given scenario. For example, an intelligence device that can sense objects below a surface of water may be required if the target object  108  is a sub-sea vehicle. A definition of a desired intelligence  210  for each timeline event  200  may be provided. For example, a definition of objects and/or activities that may to be detected and what is desired to learn about the objects and/or activities may be input into the comments, assumptions, and/or questions  212 . In an embodiment, assumptions, constraints, and/or questions related to each timeline event  200  may be included in the comments, assumptions, and questions  212 . 
         [0066]      FIG. 5  is a schematic representation of identifying intelligence methods  108 , including an exemplary mapping of identified intelligence methods  240  to one or more of the target objects  202  during each timeline event  200 . One or more of the intelligence methods  240  may be identified  108  ( FIG. 2 ) by researching platform, sensor, and/or system capabilities, as shown at reference number  108  in both  FIG. 2  and  FIG. 5 . Identifying intelligence methods  108  may include data provided by subject matter experts (SME) to identify and document available technologies. An individual entry into a database may be made by an SME for each identified intelligence and associated platform and corresponding sensor. 
         [0067]    For example, multiple entries may be made for the same capability as it applies to different release levels of a given sensor, or different entries for the same sensor on different platforms, or for the same intelligence such as communications signals intelligence (COMINT), but for different signal types. The level of detail may include minimal information to save on resources, or may include an extensive amount of information to improve results. In general, greater detail allows for a higher level of fidelity. 
         [0068]    Once an intelligence method  240  is identified, the method may be mapped to a timeline event  200  that corresponds with the intelligence method  240 . For example, the intelligence method  240  may be detecting a ship in port for the timeline event  200  to detect the target object  202  (e.g., a vessel, of a white force, entering or approaching an area of responsibility (AOR) that the white force is responsible for protecting or observing). Once the intelligence methods  240  are determined, for example researched, collected, and documented, data identifying the intelligence methods  240  may be stored and used again for additional analysis without expending resources recollecting the data. In an embodiment, a computer system stores the data identifying intelligence methods to repeatedly provide the intelligence methods for subsequent analysis. 
         [0069]    For example, the computer system may include a processor unit and a storage unit configured to perform one or more portions of one or more of the steps identified in the method of  FIG. 2 . In an embodiment, one or more of the sensor platforms  50 , for example an aircraft (as shown in  FIG. 1 , above) may include a computer or a processor unit configured to communicate with the computer performing one or more portions of the method of  FIG. 2 . In another embodiment, the computer performing the one or more portions of the method of  FIG. 2  is a plurality of computers in communication with one another through an electronic communication link, such as a global system of interconnected computer networks. 
         [0070]    The intelligence methods  240  may be periodically revisited to confirm validity and/or to address the inclusion of new technologies. For example, a computer system may re-perform the method of  FIG. 2  every day. In another embodiment, a computer system may re-perform the method of  FIG. 2  periodically within a month, a week, a day, an hour, a minute, and/or a second of a last performance of the method. Periodic repetition of the intelligence methods  240  may be based on requirements for a current mission. 
         [0071]      FIG. 6  is an exemplary mapping of capabilities  250  corresponding to platforms  260  and/or sensor  270  associated with the intelligence method  240 . A maximum number of platforms available  272 , probabilities  274 , and/or type of platform  276  may be included in the capabilities  250 . 
         [0072]    Defining which intelligence platforms  260  and/or sensors  270  may be effective to contribute to detection probabilities allows a determination of whether a given platform  260  may be included for analysis. Determination of whether a platform  260  may be effective may be based on the intelligence method  240  and/or the target object  202  ( FIG. 5 ). For example, a sensor producing a National Imagery Interpretability Rating Scale (NIIRS) image of a level  4  may be sufficient to support detecting airstrips or ships but it may not be sufficient to detect personnel servicing aircraft at an airstrip. 
         [0073]      FIG. 7  is an exemplary automatic association of a plurality of exemplary sensors  270  to a corresponding platform  260 . For example, a computer processor may match a platform  260  identification with a sensor  270  identification, based on a definition of the platform  260  that corresponds with a definition of the sensor  270 . The computer processor may perform the matching in near-real-time, thereby allowing resulting matches to be available in near-real-time. In an alternative embodiment, observation methods (e.g., platforms and/or sensors) are manually associated with desired intelligence and/or desired information. 
         [0074]      FIG. 8  is a schematic representation of researching ISR methods  122 , including an exemplary association of specific detection parameters  300  of the platform  260  for exemplary environmental conditions  302 . Researching ISR methods  122  may determine probabilities  274  associated with each identified platform  260  and sensor  270  pairing. The probabilities  274  may be determined for each intelligence method  240  and/or repeated over time to verify accurate probabilities are utilized. For example, an intelligence method  240  of detecting a ship in port may have a probability of sensor to sense or detect this observable including associated processing (P SEN     —     DET )  304  based on visibility  306 , cloud cover  308 , spectrum  310 , and/or sea state  312 . If a technology is not yet available, a notation may be made of an expected availability  278 . Parameters for the new technology may be based on SME data that considers combinations of other technologies with similar characteristics. 
         [0075]    Each detection parameter  300  may be based on the intelligence method  240  and/or the target object  202  (shown in  FIG. 5 ). For example, the parameters  300  given for one of the platforms  260  (e.g., Spc1) in combination with one of the sensors  270  (e.g., COMINT) for the intelligence method  240  of detecting a ship in port may be entirely different for a different platform  260  and sensor  270  combination, or different for detecting a tank at a landmark instead of the ship at port. In an embodiment, a computer processor may determine detection parameters in near-real-time, which allows results to be provided during a useful period of time. For example, a measure of performance (MOP) may be 90% for detecting a ship entering a port if the detection parameters can be provided within an hour, but the MOP may be 0% if the detection parameters cannot be provided for another 12 hours. If the processing time required is expected to be 12 hours, the MOP being 0% may prevent any processing of the detection parameters to be performed. If the processing time required is expected to be less than an hour, the MOP being 90% may allow processing of the detection parameters to be performed. 
         [0076]    In another embodiment, decreased processing time, for example processing of the detection parameters by an automated computer processor, allows additional platform and detection parameters to be considered in a requisite time period. 
         [0077]      FIG. 9  is a schematic representation of adding accuracy and/or timeliness parameters  126 , including an exemplary mapping of the sensors  270  with a time to process and/or report a detection (T SEN )  340 , and with an average accuracy range  342  of each sensor  270 . A probability that a platform  260  and sensor  270  combination will be available before a desired and/or requisite point in time (P TIM ) may be based on a time for the sensor  270  and T SEN    340 . 
         [0078]    For example, one of the platform  260  and sensor  270  combinations (e.g., Spc1 COMINT) may have a T SEN    340  of 20 hours. The resulting P TIM  may be 1 during a low threat scenario. Alternatively, the P TIM  may be 0 if T SEN  is 1 hour, such as when a report is necessary within 1 hour to be relevant, for example during a high threat situation. Thus, decreasing T SEN , for example during a real-time threat situation, may preclude use of multiple platform  260  and sensor  270  combinations. As mentioned above regarding required processing time and MOP, automated computer processing may reduce the time required to process and report detections from each combination to allow consideration of combinations that would otherwise take too much time to process and report. 
         [0079]      FIG. 10  is a schematic representation of integrating fusion and/or track parameters  126 , including exemplary fusion parameters  380  for an exemplary pairing of intelligence. For example, the fusion parameters may for a fix fusion  390 , which will be explained in more detail below regarding  FIG. 20 . In an embodiment, the parameters are track parameters. 
         [0080]    The fusion and/or track parameters may define a fusion performance  382  of manual fusion (e.g., performed by analysts)  384  and/or or automated fusion (e.g., performed by a computer)  386 . Including performance of the manual fusion  384  and the automated fusion  386  allows comparison of the two, which allows a determination of costs and benefits between using one over the other. 
         [0081]    The manual fusion  384  and automated fusion  386  information allows analysis of the benefits of cross-intelligence data association and fusion as well as analysis of the differences between manual and automated processing. 
         [0082]    The fusion parameters  380  allow for the unique definition of a probability of and/or resulting benefit of different intelligence products to fuse with other products. For example, two sensors  270 . A determination of a resulting change in product quality and/or time to fuse and/or report may be determined based on the fusion parameters  380 . 
         [0083]      FIG. 11  is a schematic representation of deriving the probability of detection  128 , including an exemplary set of environmental conditions  410 , a method of selecting intelligence methods  412 , loading  414  of the corresponding detection parameters  300  (shown in  FIG. 8 ), an exemplary display of parameters  300 , and a schematic of an exemplary mapping  450  (shown in  FIG. 12 ). 
         [0084]    Probabilities of detecting or providing the desired and/or required intelligence for each step of each timeline event  200  may be derived. A software program running on a computer may generate a set of intelligence methods  412 , from which the intelligence method  240  may be selected. Once the intelligence method  240  is selected, the associated detection parameters  300  are transferred into the timeline event  200  based on the environmental conditions  410  and timeliness desires and/or parameters based on the timeline event  240 . Timeliness desires and/or parameters for a given mission may be based on requirements of the mission for timeliness and for available platforms (e.g., vehicles with sensors) for parameters. 
         [0085]    A simulation modeling program  48 , such as ExtendSim illustrated schematically in  FIG. 2 , may run an simulation model based on the fusion parameters  380  to compute a high confidence value adjusting the defined parameters based on a confidence of each input parameter. For example, the confidence of each input parameter may be a standard deviation of each input parameter. ExtendSim is a commercially available tool that is utilized to model the find component  500 , fix component  502 , and track component  504  to apply user defined probability distributions to input parameters and using Monte Carlo runs to calculate the statistical results. 
         [0086]    The simulation modeling program  48  outputs may provide the tipped probabilities  36 , and failure statistics  510 . The success tipped probabilities may provide the calculated probability of detecting, fixing, and tracking the object. The failure statistics  510  may provide a count of each observation or fusion attempt failure that occurs throughout the entire analysis. The failure statistics  510  may identify key areas for potential improvement. Multiple inputs to the failure statistics  510  are illustrated in  FIG. 14 , described below. 
         [0087]    The simulation modeling program  48  model may execute a set of Monte Carlo simulation system  34  ( FIG. 2 ) runs varying the data, based on the level of confidence associated with each intelligence product and the fusion probabilities  380 . The Monte Carlo approach may account for variations in the input parameters as well as unpredictable variations in the processing and/or environmental parameters. 
         [0088]    Still referring to  FIG. 11 , once the detection parameters  300  are loaded, a number of platform  260  and sensor  270  combinations used  420  (e.g., 1 Spc1 COMINT) may be determined and/or a probability of time tasked (P TAS ) may be determined (e.g., 0). A probability of availability (P AV ) may determine whether a platform  260  and sensor  270  combination may be added. For example, a platform  260  may be in an area of interest, but not tasked to examine a specific area of interest at a particular time. 
         [0089]    The detection parameters  300  may be determined for each intelligence product, intelligence method  240 , for combined results of each target object  202  ( FIG. 3 ) and/or desired information. 
         [0090]      FIG. 12  is the schematic representation of deriving the probability of detection  128 , including an exemplary mapping of a best case probability  450  for a plurality of associated platforms  260  and sensors  270  combinations. The best case probability  450  may be determined without running a Monte Carlo analysis. 
         [0091]    The mapping of the best case probability  450  may include detection probabilities (DET)  460 . The DET  460  may be based on detection probabilities for a given intelligence (DET —INT )  462  (e.g., a platform  260  and sensor  270  combination) and/or detection probabilities for a given observable (DET —OBS )  464  (e.g., target object  202  shown in  FIG. 3 ) 
         [0092]    The DET  460  may be mapped to each event  200  to provide a simple listing of probabilities  470  for each event  200 . 
         [0093]      FIG. 13  is a schematic representation of analyzing results  140  (e.g., an intelligence value metric calculus), including a flow chart of a method of finding  500 , a method of fixing  502 , and a method of tracking  504  the target object  202  (shown in  FIG. 5 ). Results from the above analysis may be inputs for one or more of the finding  500 , fixing  502 , and/or tracking  504 . For example, the results from above may be reviewed and/or summarized. Missing or erroneous data may be reviewed and/or corrected prior to finalizing results. In an embodiment, an automated computer processor may review and or correct the missing or erroneous data. In another embodiment, a probability of correctness is determined based on the missing or erroneous data. 
         [0094]    Particularly, the parametric inputs  300 , fusion parameters  380 , timeline conditions  498  (e.g., environmental conditions  302 ), and observable information  514  (e.g., target object  202 ) may provide inputs for the finding  500 , fixing  502 , and/or tracking  504  to feed into the Monte Carlo simulation system  34 . The Monte Carlo simulation system  34  may incorporate data from the finding  500 , fixing  502 , tracking  504 , and/or an ExtendSim simulation  506  to provide tipped statistics  516  (e.g., tipped probabilities  36 , not tipped probabilities  72 , and/or false detection statistics  512 ). 
         [0095]    Determination of the tipped statistics  516  may be accomplished through analyzing the probability of detecting various observables  514  (e.g., observables  530 , other observables  532 , and non-observables  532 ) associated with the target object and/or objects  202  under analysis. The observables  530  may be the observables associated with the specific target object  202  under analysis. To detect the target object  202 , it may have one or more observables  530  associated with it. For example, to detect a ship, the observables  530  may be the ship on the surface of the water and signals intelligence radio-frequency (SIGINT RF) transmissions. The ship may be detectable by images or radar, whereas the transmissions may be detectable by an automatic identification system (AIS), COMINT, or electronic signals intelligence (“ELINT”). The other observables  532  may be the observables  514  associated with other objects that are in the area of analysis, but are not the target object  202 . An “other object” may be any object that generates observables  514  that are not associated with the targeted object  202 , but are generated within the window of analysis and will create either increased or possible conflicting observations. The non-observables  534  may be falsely detected observations that are either incorrectly detected or incorrectly associated with the target object  202 . 
         [0096]    The find analysis  500 , fix analysis  502 , and track analysis  504  form a central part of the analysis  140 , which may also include an intent analysis  508 . Any provided probabilities, along with an integration of one or more multi-source multi-intelligence detections and/or locations, may be combined determining an intent of the target object  202 . The intent analysis  508  may be a prediction of the intent of the target. The prediction may be based on results from the track analysis  504 . For example, the track analysis  504  may determine that an enemy plane has travelled 400 miles in a direction of a location A (e.g., a friendly military base). The intent analysis  508  may determine that location A is an intended destination of the enemy plane based on the previous path of the enemy plane. In an embodiment, the intent analysis may determine that the enemy plane is a threat based on a determination that the enemy plane is carrying a weapons payload capable of inflicting critical damage to the location A. In an alternative embodiment, the intent analysis may determine that an enemy invasion is high likely based on a plurality of enemy forces approaching a given location or border. 
         [0097]    The find analysis  500  addresses detecting the potential observables  514  associated with the target object  202  under analysis. The fix analysis  502  may include integrating and fusing multi-source/multi-INT detections in order to determine the benefit to improving a determination of location of the target object  202 . The track analysis  504  may include fusing multi-source/multi-INT observations over time in order to assess the probability of creating and maintaining tracks or persistent knowledge of the target object  202 . The intent analysis  508  may derive a probability of determining intent of the target object  202  based on the quality and quantity of the known observables  530 . 
         [0098]    The entity specific parametric inputs  300  and confidence &amp; quality parametric inputs  380  may include the platform  260 , sensor  270 , and observable specific input parameters that describe a probability of successfully detecting, classifying, locating, identifying, or fusing each observable under differing environmental conditions  302 . The confidence and quality parametric inputs  380  may be utilized to drive the statistical distributions in the simulation modeling program  48  runs, and/or the quality parameters may provide the timeliness and accuracy of the detected observable. The timeline conditions  498  may provide the environmental conditions  302  to account for varying sensor and processing capabilities under differing conditions such as visible light, cloud cover, RF spectrum, and sea state; to give a few non-limiting examples. 
         [0099]    Further results may be derived based on output to and feedback from identifying  520  (e.g., identifying gaps  142  ( FIG. 2 ), and identifying and/or mitigating  144  ( FIG. 2 )). Identifying analysis  520  may support analyzing track probabilities and derive a probability of determining an identity sufficient to support associating and fusing data into an existing track. 
         [0100]    Tables are provided below to provide clarity for terms used above and in the remaining portion of the present disclosure. Table 1, below, provides a list of parametric inputs and a corresponding description. Table 2 provides a list of computed probability results. Table 3 provides a list of success results associated with the detection, locating, and tracking. Table 4 provides a list of abbreviations and acronyms, and corresponding descriptions. 
         [0000]    
       
         
               
             
               
               
               
             
           
               
                 TABLE 1 
               
             
             
               
                   
               
               
                 Parametric Inputs 
               
               
                 Parametric Inputs 
               
             
          
           
               
                 Type 
                 Parameter 
                 Description 
               
               
                   
               
               
                 Observable 
                   i P OBS   
                 Probability that the observable exists where it 
               
               
                 Parameters 
                   
                 is a single observable. 
               
               
                   
                   i R OBS   
                 Rate of the observable (e.g., observations per 
               
               
                   
                   
                 1 hour period for intermittent observable 
               
               
                   
                   
                 types): 
               
               
                   
                   
                 Ship on the surface may be 1. 
               
               
                   
                   
                 Optionally, SIGINT may be turned on and off, 
               
               
                   
                   
                 or include multiple transmissions. 
               
               
                   
                   i P SEN   
                 Probability that the sensor is able to sense 
               
               
                   
                   
                 when coupled with  i R OBS . 
               
               
                   
                   i R SEN   
                 Rate of the sensor to attempt to detect the 
               
               
                   
                   
                 observable when coupled with  i P OBS . 
               
               
                   
                   i N TARGET     —     OBJECTS   
                 Number of target objects in the field of 
               
               
                   
                   
                 analysis. 
               
               
                   
                   i N OTHER     —     OBJECTS   
                 Number of other objects in the field of 
               
               
                   
                   
                 analysis (e.g., similar objects that are not the 
               
               
                   
                   
                 specific targeted objects of interest). 
               
               
                 Sensor 
                   i P SEN     —     AV   
                 Probability of the sensor being physically 
               
               
                 Parameters 
                   
                 available (e.g., in the area and capable of 
               
               
                   
                   
                 sensing the observable). 
               
               
                   
                   i P SEN     —     TASK   
                 Probability of sensor being tasked to collect or 
               
               
                   
                   
                 detect this observable. 
               
               
                   
                   i P SEN     —     DET   
                 Probability of the sensor to sense or detect 
               
               
                   
                   
                 the observable, and may include associated 
               
               
                   
                   
                 processing. The probability may be expanded 
               
               
                   
                   
                 separately for environmental conditions. 
               
               
                   
                   i P SEN     —     CLSFY   
                 Probability of the sensor being tasked to 
               
               
                   
                   
                 produce a sufficient identification or typing to 
               
               
                   
                   
                 support fusion for the observable, if the 
               
               
                   
                   
                 observable is detected. 
               
               
                   
                   i P SEN     —     LOC   
                 Probability of the sensor being tasked to 
               
               
                   
                   
                 produce a sufficient location to benefit fusion 
               
               
                   
                   
                 for the observable, if the observable is 
               
               
                   
                   
                 detected and classified. 
               
               
                   
                   i T SEN   
                 Time for the sensor and associated 
               
               
                   
                   
                 processing to generate a detection (e.g., time 
               
               
                   
                   
                 available to report in hours). 
               
               
                   
                   i Q SEN   
                 Quality of the produced product (e.g., how 
               
               
                   
                   
                 well it contributes to the find, fix, track 
               
               
                   
                   
                 process), which may focus on an overall 
               
               
                   
                   
                 quality of the detected location. 
               
               
                   
                   
                 0-1: 0 may signify no contribution. 1 
               
               
                   
                   
                 may signify a provided ID and sufficient 
               
               
                   
                   
                 location for the detection, on its own, to 
               
               
                   
                   
                 provide the location of the detection, a 
               
               
                   
                   
                 % between 0 and 1 to identify the 
               
               
                   
                   
                 overall quality as defined in Table 5, 
               
               
                   
                   
                 below. 
               
               
                   
                   i R SEN   
                 Cycle Rate of the sensor corresponding to the 
               
               
                   
                   
                 observable (e.g., effective sensed sample 
               
               
                   
                   
                 rate). 
               
               
                   
                   
                 SIGINT - 1 detect per sensor per 
               
               
                   
                   
                 observable (although multiple sensors 
               
               
                   
                   
                 make be used and combined). 
               
               
                   
                   
                 Imagery - 1 to n detects per take or 
               
               
                   
                   
                 frame rate of the sensor. 
               
               
                   
                   i N SEN   
                 Number of sensors. 
               
               
                 Sensor 
                   i RFD 
                 Rate of false detections over time for this 
               
               
                 False 
                   
                 sensor and associated processing, (e.g., RFD 
               
               
                 Detection 
                   
                 may indicate that 1.5 false detections will be 
               
               
                   
                   
                 generated per day). 
               
               
                   
                   i PFD 
                 Probability of false detections per valid 
               
               
                   
                   
                 detections for this sensor and associated 
               
               
                   
                   
                 processing (e.g., 2% PFD indicates 2 out of 
               
               
                   
                   
                 100 detections are false). 
               
               
                 Fix Fusion 
                   i P FIX     —     PR     —     SUCCESS   
                 Probability of successful fusion across an INT 
               
               
                 Parameters 
                   
                 pair. 
               
               
                   
                   i T FIX     —     AVG     —     FUSE   
                 Average time for the data combination to fuse 
               
               
                   
                   
                 Including queue time awaiting processing and 
               
               
                   
                   
                 analysis time to fuse the data. 
               
               
                   
                   i Q FIX     —     PR     —     SUCCESS   
                 Average quality improvement of a successful 
               
               
                   
                   
                 fusion across an INT pair represented as a % 
               
               
                   
                   
                 of improvement (e.g., .1 represents a 10% 
               
               
                   
                   
                 improvement of the location over the best 
               
               
                   
                   
                 quality in the INT pair). It is possible for the 
               
               
                   
                   
                 improvement to be 0% such that the fusion of 
               
               
                   
                   
                 the data is of value for confirmation (e.g., 
               
               
                   
                   
                 supports the tracking process) but does not 
               
               
                   
                   
                 improve the overall quality of the location. 
               
               
                   
                   i N ANALYSTS   
                 Number of fix fusion analysts. 
               
               
                   
                   i N FUSION     —     ENGINES   
                 Number of fix fusion engines to support 
               
               
                   
                   
                 automated fusion. 
               
               
                 Other 
                   i T INTERVAL   
                 Total analysis period (e.g., hours). 
               
               
                 Parameters 
                   i T SLICE   
                 Time sub-interval used by a simulation 
               
               
                   
                   
                 modeling program for collecting detection to 
               
               
                   
                   
                 fuse. 
               
               
                   
               
             
          
         
       
     
         [0000]    
       
         
               
             
               
               
               
             
           
               
                 TABLE 2 
               
             
             
               
                   
               
               
                 Computed Results Description Table 
               
             
          
           
               
                 Type 
                 Parameter 
                 Description 
               
               
                   
               
               
                 Single 
                 P, T, Q, N DET     —     OBS (o) 
                 For each observable (o). 
               
               
                 Observable 
                 P 
                 Probability of detection. 
               
               
                 Detection 
                 T 
                 Time to detect and/or process 
               
               
                   
                   
                 to generate a detection. 
               
               
                   
                 Q 
                 Quality of the detection. 
               
               
                   
                 N 
                 Number of single intelligence 
               
               
                   
                   
                 detections. 
               
               
                 INT Detection 
                 P, T, Q, N DET     —     INT (i) 
                 All observables within the 
               
               
                   
                   
                 intelligence - for each INT 
               
               
                   
                   
                 (i). 
               
               
                 Across all 
                 P, T, Q, N DET   
                 Best case available 
               
               
                 INT Detections 
                   
                 performance (e.g., for the 
               
               
                   
                   
                 analysis period) 
               
               
                 Fused Pair 
                 P, T, Q, N FIX     —     CMBO (c) 
                 For each fix fusion 
               
               
                 Combinations 
                   
                 combination (c) across all 
               
               
                 across INTs 
                   
                 types. 
               
               
                 Fix Fusion 
                 P, T, Q, N FIX     —     FUSE   
                 Fused combinations across all 
               
               
                   
                   
                 data sets (e.g., for the 
               
               
                   
                   
                 analysis period) 
               
               
                   
                 P FIX   
                 Overall probability of an 
               
               
                   
                   
                 improved location based on 
               
               
                   
                   
                 fused data. 
               
               
                 Track Fusion 
                 P, T, Q, N TRACK     —     FUSE   
                 Track across all find and fix 
               
               
                   
                   
                 results (e.g., for the analysis 
               
               
                   
                   
                 period). 
               
               
                   
                 P TRACK   
                 Overall probability of 
               
               
                   
                   
                 maintaining track on the 
               
               
                   
                   
                 object based on all available 
               
               
                   
                   
                 intelligence products. 
               
               
                   
               
             
          
         
       
     
         [0000]    
       
         
               
             
               
               
               
             
           
               
                 TABLE 3 
               
             
             
               
                   
               
               
                 Success Probability Description 
               
             
          
           
               
                 Type 
                 Parameter 
                 Description 
               
               
                   
               
               
                 Detections 
                 P DET   
                 Probability of successful detection across all 
               
               
                   
                   
                 INT groups. 
               
               
                   
                 T DET   
                 Average time for the associated data to be 
               
               
                   
                   
                 available for fusion. 
               
               
                   
                 Q DET     —     LOC   
                 Location quality associated with the P DET . 
               
               
                   
                 R DET   
                 Number of true detected products forwarded 
               
               
                   
                   
                 excluding not false detections. 
               
               
                 Locations 
                 P FUSE   
                 Probability of successful fusion for a specific 
               
               
                   
                   
                 INT pair combination. 
               
               
                   
                 T FUSE   
                 Average time for the combined fusion to be 
               
               
                   
                   
                 available. 
               
               
                   
                 Q LOC   
                 Probability of successful location associated 
               
               
                   
                   
                 with the P FUSE . 
               
               
                   
                 P FIX   
                 Probability of a successful fix across all 
               
               
                   
                   
                 available INTs. 
               
               
                   
                 T FIX   
                 Average time for the final fused result to be 
               
               
                   
                   
                 available. 
               
               
                   
                 R FIX   
                 Number of fused products forwarded. 
               
               
                 Track 
                 P TRACK   
                 Probability of a successful track. 
               
               
                   
               
             
          
         
       
     
         [0000]    
       
         
               
             
               
               
               
             
           
               
                 TABLE 4 
               
             
             
               
                   
               
               
                 Abbreviations and Acronyms 
               
             
          
           
               
                   
                 Acronym 
                 Description 
               
               
                   
                   
               
               
                   
                 AIS 
                 Automatic Identification System 
               
               
                   
                 AMD 
                 Asymmetric Missile Defense 
               
               
                   
                 BF 
                 Blue Force 
               
               
                   
                 BFA 
                 Blue Force Asset 
               
               
                   
                 COMINT 
                 Communications Signals Intelligence 
               
               
                   
                 ELINT 
                 Electronic Signals Intelligence 
               
               
                   
                 EO 
                 Electrical Optical 
               
               
                   
                 FISINT 
                 Foreign Instrumentation Signals INTelligence 
               
               
                   
                 FMV 
                 Full Motion Video 
               
               
                   
                 GMTI 
                 Ground Moving Target Indicator 
               
               
                   
                 HUMINT 
                 Human Intelligence 
               
               
                   
                 INT 
                 Intelligence 
               
               
                   
                 IR 
                 Infrared 
               
               
                   
                 ISR 
                 Intelligence, Surveillance and Reconnaissance 
               
               
                   
                 MOE 
                 Measures Of Effectiveness 
               
               
                   
                 NATO 
                 North Atlantic Treaty Organization 
               
               
                   
                 NIS 
                 NATO Identification System 
               
               
                   
                 OBS 
                 Observations Set or Set of Observations 
               
               
                   
                 PED 
                 Processing, Exploitation and Dissemination 
               
               
                   
                 PIFBA 
                 Probability Integration Fusion Benefit Analysis 
               
               
                   
                 RCAF 
                 Royal Canadian Air Force 
               
               
                   
                 RIVMC 
                 Raytheon Intelligence Value Metric Calculus 
               
               
                   
                 RFT 
                 Red Force Threat 
               
               
                   
                 SAG 
                 Surface Action Group 
               
               
                   
                 SME 
                 Subject Matter Expert 
               
               
                   
                 STANAG 
                 Standardization Agreement 
               
               
                   
                 VT 
                 Vulnerability-Techniques 
               
               
                   
                   
               
             
          
         
       
     
         [0101]      FIG. 14  is a detailed flow chart of the methods of  FIG. 13 . Table 1, above, provides a detailed description of the individual input parameters  600  (e.g., iP SEN     —     AV  and iP SEN     —     TASK ) and computed output probability parameters (e.g., P DET    610 , P FIX    612 , P TRACK    614 , etc.).  FIG. 15  is a flow chart of the method of finding  500 . 
         [0102]    The find analysis  500  may analyze the probability of successful detections, including the number of detections, timeliness, quality, and failure statistics shown in  FIG. 15 . Tables 1-3 provide a description of the input parameters  600  and the failure statistics  602 . Detections may be derived by sensing  630 , tasking  632 , detecting  634 , classifying  636 , and/or locating  638 . A determination of a number of occurrences and interval of an occurrence may be determined based on each observable  530  to provide one or more of the input parameters  600 . 
         [0103]    Sensing  630  may determine the probability that the platform  260  and sensor  270  are available in the area of interest and that the target object  202  is in range or field of view of the sensor. Tasking  632  may determine the probability that the sensor  270  is tasked to detect the target object  202 . Detecting  634  may determine the probability that the sensor  270  and associated processing will detect the target object  202  under the current environmental conditions  302  as defined in the timeline conditions  498 . Classifying  636  may determine the probability that the detection provides sufficient object classification to allow the detection to be associated with either this target object  202  or this class of objects. Locating  638  may determine the probability that the detection provides the location of the target object  202  and to what quality or accuracy. 
         [0104]    Detections  650  may include integrating together all the detections of each intelligence tool (e.g., platform  260  and/or sensor  270 ), and then integrating together the probability, timeliness, and/or quality of all detections within the analysis window to provide the overall probability of detecting the target object  202 . The average timeliness of successful detections may be provided to determine whether enough time is available to implement an intelligence tool (e.g., a platform  260  and/or analysis). 
         [0105]    Analyzing results  140  may include an intelligence value metric calculus, which may be based on one or more sets of equations to derive the finding analysis  500 , fixing analysis  502 , and/or the tracking analysis  504 . Details of an exemplary embodiment of the equations are discussed below. The finding analysis  500  may be derived from a single observable input, a single observable detection, a single intelligence detection, and/or a detection across intelligence groups. 
       Equation Set 1: Single Observable Input 
       [0106]    Single observable input equations may provide measures of effectiveness (MOEs) derived through a combination of initial user inputs (denoted with the letter i preceding the equation variable) and computed quantities that may be derived from these inputs. The method by which user inputs are converted to probabilities is important to determining the confidence intervals associated with these MOEs. 
         [0107]    A number of potential observables is (N OBS (o)) for each target object  202  ( FIG. 5 ). Sensor  270  ( FIG. 6 ) pairing is based on a rate of the observable occurrence (iR OBS ), a rate of the sensor  270  to attempt to detect (iR SEN ), and a number of sensors (iN SEN ) being used. A number of opportunities to detect for a single observable object may be derived from the number of potential observables, rate of observable occurrence, and rate of the sensor to attempt detection. 
         [0108]    A number of potential observables for intelligence types that generate observations (e.g., EO and IR) may be determined with based on following equation: 
         [0000]        N   OBS ( o )= iP   OBS   *iR   SEN   *iN   SEN   *iT   INTERVAL   (1.1)
 
         [0109]    A number of potential observables for intelligence types that provide surveillance of a region, looking for generated activity (e.g., SIGINT) may be determined based on the following equation: 
         [0000]        N   OBS ( o )= iR   OBS   *iP   SEN   *iN   SEN   *iT   INTERVAL   (1.2)
 
         [0110]    The total number of opportunities to detect observable objects is increased by the number of targeted and non-target or other objects in the sensor(s)  270  field of view. A target object is an object that is of the same type and characteristics of the target object  202  (e.g., an object the find analysis  500 , fix analysis  502 , and track analysis  504  determine probabilities for). For example, if a surface action group (SAG) containing 4 ships is targeted, then the number of targeted objects (iN TARGETED     —     OBJECTS ) is 4. If there are 6 commercial vessels and a force ship in the area, then the number of other similar objects that may generate observables  514  (iN OTHER     —     OBJECTS ) is 7. 
         [0111]    Therefore while the analysis is to determine the probability of successfully finding and tracking a single ship, the overall processing and fusion effort may deal with many observables of non-interest, such as other observables  532  and non-observables  534  ( FIGS. 13 and 14 ). A total number of observations may increase, which may greatly increase the number of items that the analyst or automated system (e.g., a computer system or a computer network) has to process in the later stages of fixing  502  and tracking  504 . Each of these observables  514  (N TOT     —     OBS (o)) represents an observation opportunity, which can be determined from the following equation: 
         [0000]        N   TOT     —     OBS ( o ) =N   OBS ( o )*( iN   TARGET     —     OBJECTS   +iN   OTHER     —     OBJECTS )  (1.3)
 
         [0112]    An interval between observation opportunities and a rate of observation opportunities may affect the ability to fuse and the probability of a successful fusion. 
         [0113]      FIG. 16  is a flow chart of a single observable detection portion of the method of finding of  FIG. 13 . Equation set 2 provides equations associated with detecting the single observable from a single sensor. 
       Equation Set 2: Single Observable Detection 
       [0114]    Single observable detection equations may provide a probability of detecting a single object based on the following parameters described in Table 1, above: iP SEN     —     AV    670 ; iP SEN     —     TASK    672 ; iP SEN     —     DET    674 ; iP SEN     —     CLSFY    676 ; and/or iP SEN     —     LOC    678 . 
         [0115]    A probability of a single observable being detected assuming the observable occurs (e.g., P OCC  of 1) may be determined based on the following equation: 
         [0000]        P   SINGLE     —     DETECT =( iP   SEN     —     AV   *iP   SEN     —     TASK   *iP   SEN     —     DET   *iP   SEN     —     CLSFY   *iP   SEN     —     LOC )  (2.1)
 
         [0116]    The probability of detecting the object is increased by having additional time (iT INTERVAL ) and additional observables or additional sensors to increase the opportunities to detect the object (N OBS ). Equation 1.1 calculated the number of observables (N OBS (o)) based upon the rate of the observable, the sensor cycle rate, and the number of sensors. Equation 2.2 may adjust the single detect probability, assuming independent observations and time independence, for these increased detection opportunities based on the following equation: 
         [0000]        P   DET     —     OBS ( o )=1−(1−( P   SINGLE     —     DETECT )) (iT     INTERVAL   * N     OBS     (o))   (2.2)
 
         [0117]    Due to sensor motion, the actual values of iP SEN     —     AV , iP SEN     —     TASK , and iP SEN     —     DET  vary with time, based on a relative location of the sensors  270  and the target object  202 . The values may represent expected and/or average values across the observation period, which allows an assumption that multiple observations represent independent events with equal probabilities of detection. A dependence of detection probabilities on time (e.g., time of day) can be evaluated by generating time and environment dependent values for iP SEN     —     AV , iP SEN     —     TASK , and iP SEN     —     DET , iP SEN     —     CLSFY  and iP SEN     —     LOC  are not required to detect an object, but may be provided as described to support the detection analysis  500 , as well as the fixing analysis  502  and the tracking analysis  504 . 
         [0118]    Additional parameters associated with the single detections may include an average time to detect and process each observable (T DET     —     OBS (o)), average location data quality of each observable (Q DET     —     OBS (o)), and the expected number of observables detected for a single object (N DET     —     SINGLE     —     OBS (o)). T DET     —     OBS (o)), Q DET     —     OBS (O), and N DET     —     SINGLE     —     OBS (o) may be based on equations 2.3, 2.4, and 2.5, following: 
         [0000]        T   DET     —     OBS ( o )=the detection and processing time( iT   SEN ( o ))  (2.3)
 
         [0000]        Q   DET     —     OBS ( o )=the location data quality( iQ   SEN ( o ))  (2.4)
 
         [0000]    Quality refers to a level of accuracy with which observables are detected, fused, and tracked to derive ISR data products that reflect a desired output relative to an end user&#39;s data quality requirement (e.g., a set of mission objectives). The term accuracy is an average resolution for the ISR data product measurements. For example when the desired end product is a geolocation coordinate, then the accuracy is a set coordinate and the associated unit measurement within which those coordinates can be sited with confidence (e.g., within 1 mile). Table 5, below, describes an exemplary quality measurement normalization across intelligence products based on the product average accuracy. 
         [0000]    
       
         
               
             
               
               
               
               
               
             
               
               
               
               
               
               
               
               
             
           
               
                 TABLE 5 
               
             
             
               
                   
               
               
                 Quality Metric Description 
               
               
                 Quality Metric Description 
               
             
          
           
               
                 iQ SEN   
                 SEN 
                 Meters 
                 Miles 
                 Nautical Miles 
               
               
                   
               
             
          
           
               
                 1.00 
                 if &lt; 
                 1 
                 m 
                 3 
                 feet 
                 3 
                 feet 
               
               
                 0.95 
                 if &lt; 
                 2 
                 m 
                 7 
                 feet 
                 7 
                 feet 
               
               
                 0.90 
                 if &lt; 
                 4 
                 m 
                 13 
                 feet 
                 13 
                 feet 
               
               
                 0.85 
                 if &lt; 
                 8 
                 m 
                 26 
                 feet 
                 26 
                 feet 
               
               
                 0.80 
                 if &lt; 
                 16 
                 m 
                 17 
                 yards 
                 17 
                 yards 
               
               
                 0.75 
                 if &lt; 
                 32 
                 m 
                 35 
                 yards 
                 35 
                 yards 
               
               
                 0.70 
                 if &lt; 
                 64 
                 m 
                 70 
                 yards 
                 70 
                 yards 
               
               
                 0.65 
                 if &lt; 
                 128 
                 m 
                 140 
                 yards 
                 140 
                 yards 
               
               
                 0.60 
                 if &lt; 
                 256 
                 m 
                 280 
                 yards 
                 280 
                 yards 
               
               
                 0.55 
                 if &lt; 
                 512 
                 m 
                 560 
                 yards 
                 560 
                 yards 
               
               
                 0.50 
                 if &lt; 
                 1.024 
                 km 
                 0.64 
                 miles 
                 0.55 
                 NM 
               
               
                 0.45 
                 if &lt; 
                 2.048 
                 km 
                 1.27 
                 miles 
                 1.11 
                 NM 
               
               
                 0.40 
                 if &lt; 
                 4.096 
                 km 
                 2.55 
                 miles 
                 2.21 
                 NM 
               
               
                 0.35 
                 if &lt; 
                 8.192 
                 km 
                 5.09 
                 miles 
                 4.42 
                 NM 
               
               
                 0.30 
                 if &lt; 
                 16.384 
                 km 
                 10.18 
                 miles 
                 8.85 
                 NM 
               
               
                 0.25 
                 if &lt; 
                 32.768 
                 km 
                 20.36 
                 miles 
                 17.69 
                 NM 
               
               
                 0.20 
                 if &lt; 
                 65.536 
                 km 
                 40.72 
                 miles 
                 35.39 
                 NM 
               
               
                 0.15 
                 if &lt; 
                 131.072 
                 km 
                 81.44 
                 miles 
                 70.77 
                 NM 
               
               
                 0.10 
                 if &lt; 
                 262.144 
                 km 
                 162.89 
                 miles 
                 141.55 
                 NM 
               
               
                 0.05 
                 if &lt; 
                 524.288 
                 km 
                 325.78 
                 miles 
                 283.09 
                 NM 
               
               
                 0.00 
               
               
                   
               
             
          
         
       
     
         [0000]        N   DET     —     SINGLE     —     OBS ( o )=( N   OBS ( o ) *P   SINGLE     —     DETECT )  (2.5.1)
 
         [0119]    The number of detections may be the max number of detections (N OBS (o)) scaled by the probability of detecting the object (P SINGLE     —     DETECT ). The total number of detections may be multiplied by the number of target objects  202  that are in the sensor range (iN TARGET     —     OBJECTS ) to determine the total number of detections (N DET     —     OBS (o)). 
         [0000]        N   DET     —     OBS ( o )=( N   DET     —     SINGLE     —     OBS ( o )* iN   TARGET     —     OBJECTS )  (2.5.2)
 
         [0120]    Each sensor may have different sensing characteristics (e.g., visibility, cloud cover, spectrum, and sea state) and may be affected by variations in aspects of the sensing environment. Table 6 is an exemplary list of information about each sensor and a plurality of variables that affect each sensor. This table shows the environmental parameters for each sensor type and how they are utilized to calculate the iP SEN     —     DET  value used in equation 2.1 above. 
         [0000]    
       
         
               
             
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
               
               
               
             
           
               
                 TABLE 6 
               
             
             
               
                   
               
               
                 P SEN  Intelligence Selection Table 
               
             
          
           
               
                   
                 Visibility 
                 Cloud 
                 Spectrum 
                   
               
             
          
           
               
                   
                 Day/ 
                   
                 Cover 
                   
                 Noisy/ 
                   
                 Sea 
                   
               
             
          
           
               
                 INT 
                 P SEN   
                 Night 
                 Clear 
                 Partial 
                 Overcast 
                 Clear 
                 Spoof 
                 Jammed 
                 &lt;=3 
                 &gt;3 
                 Formula 
               
               
                   
               
               
                 COMINT 
                   
                   
                   
                   
                   
                 X 
                 X 
                 X 
                   
                   
                 Select P SEN  based solely on 
               
               
                 ELINT 
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 Spectrum environment (e.g., clear is 
               
               
                 FISINT 
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 default) 
               
               
                 EO 
                 Use 
                 P d   
                 P d   
                 P d   
                 P d   
                   
                   
                   
                 P d   
                 P d   
                 *If sea state is null then sea state = 
               
               
                   
                 cloud 
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 1. 
               
               
                   
                 cover 
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 *If cloud cover is null then clear is 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 default. 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 *If night then night*sea state. 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 If day, then cloud cover*sea state. 
               
               
                 IR 
                 Use 
                 P d   
                 P d   
                 P d   
                 P d   
                   
                   
                   
                 P d   
                 P d   
                 *same as EO. 
               
               
                   
                 cloud 
               
               
                   
                 cover 
               
               
                 SAR 
                 Use 
                 P d   
                 P d   
                 P d   
                 P d   
                 X 
                 {circumflex over ( )} 
                 {circumflex over ( )} 
                 P d   
                 P d   
                 Only affected if jam is in the same 
               
               
                   
                 cloud 
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 spectrum. 
               
               
                   
                 cover 
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 {circumflex over ( )}degraded. 
               
               
                 OPIR 
                   
                   
                 P d   
                 P d   
                 P d   
                   
                   
                   
                   
                   
                 *If sea state is null, then sea state = 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 1. 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 *If cloud cover is null, then clear is 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 the default. 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 *If day, then cloud cover*sea state. 
               
               
                 LIDAR 
                   
                   
                 P d   
                 P d   
                 P d   
                   
                   
                   
                 P d   
                 P d   
                 Only affected if jam is in the same 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 spectrum. 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 {circumflex over ( )}degraded. 
               
               
                 AIS 
                   
                   
                   
                   
                   
                 X 
                 X 
                 X 
                   
                   
                 *Select P SEN  based solely on 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 spectrum environment. 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 Clear is the default. 
               
               
                 Acoustic 
                 X 
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 *Use day/P SEN  value. 
               
               
                 FMV 
                 X 
                 X 
                 X 
                 X 
                 X 
                   
                   
                   
                 X 
                 X 
                 *If sea state is null then sea state = 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 1. 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 *If cloud cover is null then clear is 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 default. 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 *If night then night*sea state. 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 If day, then cloud cover*sea state. 
               
               
                 MTI 
                 X 
                 X 
                 X 
                 X 
                 X 
                 X 
                 {circumflex over ( )} 
                 {circumflex over ( )} 
                 X 
                   
                 Only affected if jam is in the same 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 spectrum. 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 {circumflex over ( )}degraded. 
               
               
                 HIS/MSI 
                 X 
                 X 
                 X 
                 X 
                   
                   
                   
                   
                 X 
                   
                 *If sea state is null then sea state = 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 1. 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 *If cloud cover is null then clear is 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 default. 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 *If night then night*sea state. 
               
               
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 If day, then cloud cover*sea state. 
               
               
                 OSINT 
                 X 
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 *Use day/P SEN  value. 
               
               
                 HUMINT 
                 X 
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 *Use day/P SEN  value. 
               
               
                 Cyber 
                 X 
                   
                   
                   
                   
                   
                   
                   
                   
                   
                 *Use day/P SEN  value. 
               
               
                   
               
             
          
         
       
     
         [0121]      FIG. 17  is a flow chart of a single intelligence detection portion of the method of finding of  FIG. 13 . Equation set 3, described below, provides equations associated with integrating, across sensors within an intelligence type, probabilities of detection for that intelligence type. 
       Equation Set 3: Single Intelligence Detection 
       [0122]    Aggregating metrics associated with like sensors against the same observable such as three different sensors all attempting to image the same occurrence of the observable. This allows for the use of multiple sensors to provide intelligence type data to be used during a later fixing analysis  502 . 
         [0123]    P DET     —     INT (i)  700  is a probability of a successful detection from any of the sensors to provide intelligence type. P DET     —     INT (i)  700  is also the probability that any of the sensors were successful in detecting the observable. A successful detection is equal to 1 minus the product of all individual failures, for each intelligence type (i) in the set of intelligence types from each observable (o) from the available set OBS of all observables for each intelligence type (i), given by the following equation: 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       
                         
                           
                             P 
                             
                               DET 
                                
                               
                                   
                               
                                
                               _ 
                                
                               
                                   
                               
                                
                               INT 
                             
                           
                            
                           
                             ( 
                             i 
                             ) 
                           
                         
                         = 
                           
                          
                         
                           1 
                           - 
                           
                             ( 
                             
                               
                                 ( 
                                 
                                   1 
                                   - 
                                   
                                     
                                       P 
                                       
                                         DET 
                                          
                                         
                                             
                                         
                                          
                                         _ 
                                          
                                         
                                             
                                         
                                          
                                         OBS 
                                       
                                     
                                      
                                     
                                       ( 
                                       1 
                                       ) 
                                     
                                   
                                 
                                 ) 
                               
                               * 
                               
                                 ( 
                                 
                                   1 
                                   - 
                                   
                                     
                                       P 
                                       
                                         DET 
                                          
                                         
                                             
                                         
                                          
                                         _ 
                                          
                                         
                                             
                                         
                                          
                                         OBS 
                                       
                                     
                                      
                                     
                                       ( 
                                       2 
                                       ) 
                                     
                                   
                                 
                                 ) 
                               
                               * 
                               … 
                             
                           
                         
                       
                     
                   
                   
                     
                       
                           
                          
                         
                           ( 
                           
                             1 
                             - 
                             
                               
                                 P 
                                 
                                   DET 
                                    
                                   
                                       
                                   
                                    
                                   _ 
                                    
                                   
                                       
                                   
                                    
                                   OBS 
                                 
                               
                                
                               
                                 ( 
                                 n 
                                 ) 
                               
                             
                           
                           ) 
                         
                         ) 
                       
                     
                   
                   
                     
                       
                         = 
                           
                          
                         
                           1 
                           - 
                           
                             ( 
                             
                               
                                 ∏ 
                                 
                                   o 
                                   ∈ 
                                   OBS 
                                 
                               
                                
                               
                                 ( 
                                 
                                   1 
                                   - 
                                   
                                     
                                       P 
                                       
                                         DET 
                                          
                                         
                                             
                                         
                                          
                                         _ 
                                          
                                         
                                             
                                         
                                          
                                         OBS 
                                       
                                     
                                      
                                     
                                       ( 
                                       o 
                                       ) 
                                     
                                   
                                 
                                 ) 
                               
                             
                             ) 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   3.1 
                   ) 
                 
               
             
           
         
       
     
         [0124]    Equation 3.1 may assume that each different sensor detection is independent. Assuming independence is not entirely realistic, for example, different SIGINT collectors may all be affected by the power of a transmitted signal, but this assumption allows simplification of the association of different sensors to observable events. 
         [0125]    The expected time for each intelligence type&#39;s result to be reported as well as available for fusion T DET     —     INT  (i)  702  is based on a weighted average time for each intelligence type&#39;s detection and processing. The level of each intelligence type&#39;s contribution to a successful result is used as the weighting factor in equation 3.2 as follows: 
         [0000]        T   DET     —     INT ( i )=Σ oεOBS ( N   DET     —     OBS ( o ) *T   DET     —     OBS ( o ))/Σ oεOBS   N   DET     —     OBS ( o )  (3.2)
 
         [0126]    Also, the expected data quality of each intelligence product may be based on the weighted average of each individual observable&#39;s potential contribution to the result as follows in equation 3.3: 
         [0000]        Q   DET     —     INT ( i )=Σ oεOBS ( N   DET     —     OBS ( o )* Q   DET     —     OBS ( o ))/Σ oεOBS   N   DET     —     OBS ( o )  (3.3)
 
         [0127]    The expected total number of observable available for each INT (i) is given by the sum of each INT&#39;s observables in equation 3.4 as follows: 
         [0000]        N   DET     —     INT ( i )=Σ oεOBS ( N   DET     —     OBS ( o ))  (3.4)
 
         [0000]      FIG. 18  is a flow chart of a group of intelligence detection analysis blocks to support the method of finding of  FIG. 13 . Equations set 4, described below, provides equations associated with a probability of overall detections against the target object  202 . This step may not involve improvement of the intelligence product, just the overall probability P DET    610  and number of detections N DET    714  that are expected. 
       Equation Set 4: Detection Across INT Groups 
       [0128]    The best case probability of successful detection across all INT groups P DET    610  may be based on the following equation: 
         [0000]        P   DET =1−(Π iεINTs (1 −P   DET     —     INT ( i ))  (4.1)
 
         [0129]    As with the above equations, equation 4.1 assumes that the different sensor detections are independent. 
         [0130]    A weighted average time may be factored based on the probability and number of detections (N DET     —     INT ) for each intelligence type, for example, as provided below in equation 4.2. The number of detections for each intelligence type (N DET     —     INT ) may be based on the individual probability of detection for that observable with each sensor (P SINGLE     —     DETECT )) equation 2.1 above. 
         [0000]        T   DET =Σ iεINTs ( N   DET     —     INT ( i ) *T   DET     —     INT ( i ))/Σ iεINTs   N   DET     —     INT ( i )  (4.2)
 
         [0131]    A weighted average location data quality across all intelligence types may be given by provided by equation 4.3 as follows: 
         [0000]        Q   DET =Σ iεINTs ( N   DET     —     INT ( i )* Q   DET     —     INT ( i ))/Σ iεINTs   N   DET     —     INT ( i )  (4.3)
 
         [0132]    A total number of expected detects across all intelligence types may be provided by the equation 4.4 as follows: 
         [0000]        N   DET =Σ iεINTs ( N   DET     —     INT ( i ))  (4.4)
 
         [0000]      FIG. 19  is a flow chart of the method of fixing of  FIG. 13 . The fixing analysis  502  (also referred to as “fix fusion analysis”) may analyze a manual fix fusion  720  and an automated fix fusion  722  to improve the quality of the detected location. Fix fusion analysis  502  allows a determination of a probability of fusing multi-intelligence detections in order to improve the resulting location or quality of the detection. For example, the fix fusion analysis  502  may determine a fixed location of a single object, such as an enemy aircraft or a traffic light closest to a building. Fix fusion analysis  502  may compare effects and/or benefits of automated fusion  722  versus manual fusion  720  based upon given input parameters. The fusion quality can be negatively affected by the number of non-observables  534  detected and other observables  532  detected that can reduce the number of true detections that can be processed in the given time window, and can degrade the quality of the final location. The definition of the input parameters and failure statistics are presented in Tables 1-3 above. 
         [0133]      FIG. 20  is a flow chart of the method of tracking of  FIG. 13 . The tracking analysis  504  (also referred to as “track fusion analysis”) may analyze a manual track fusion  730  and an automated track fusion  732 . Track fusion analysis  504  may estimate a probability of creating or sustaining tracks on a target object  202  ( FIG. 5 ). Track fusion analysis  504  may involve a longer term association and persistence of knowledge related to the target object of interest. For example, track fusion analysis  504  may track a path of the target object  202 , such a path of connecting traffic lights from a building A to a building B. Track fusion analysis  504  may involve a detection resulting from fix fusion analysis  502  and/or an individual detection that failed fix fusion analysis  502 . High quality classification and/or identification allow association of each detection with the target object  202 . A quality threshold may provide a minimum quality classification that must be met, based on each target of interest and mission requirement. For example, a day with cloud cover may result in low quality images, whereas a day with clear skies may result in high quality images. A mission requirement of merely detecting a piece of land or water may not require a high quality image, such as an image taken from a satellite during a day with some cloud cover. Alternatively, if the mission requirement is to detect numbers on a license plate of a vehicle, a clear day may be required. 
         [0134]    The identifying  520  may analyze a probability of improving quality of detection classification information and/or an identification to support associating detections during the track fusion analysis  504 . 
         [0135]    Referring again to  FIG. 2 , identifying gaps  142  may include identifying steps in the timeline of events  104  where a probability of obtaining the desired intelligence is not sufficient to meet a mission requisite. Before attempting to identify gap mitigation  144 , reviewing the simulation modeling program  48  “Not Tipped” values (or failure statistics) may indicate missed opportunities. Identifying a highest opportunity for increased performance and then addressing a lack of probability of success may indicate what changes to make. For example, if 90% of the tip failures are because no sensor was available to detect the target object  202 , then adding sensors to may determine the required probability is met. Alternatively, if 90% of the tip failures are because the system failed to sense the target object  202 , then providing a different sensor or technology may increase successful tipping. Table 7 shows the list of “Not Tipped” failure values with a description, generic potential causes, and mitigation steps. 
         [0000]    
       
         
               
             
               
               
               
             
           
               
                 TABLE 7 
               
             
             
               
                   
               
               
                 Not Tipped Failure Statistics With Potential Causes 
               
             
          
           
               
                   
                   
                 Description with Generic Causes and 
               
               
                 Type 
                 Parameter 
                 Mitigation 
               
               
                   
               
               
                 Detec- 
                 F AV   
                 Observable not detected because the 
               
               
                 tion 
                   
                 sensor was not available. 
               
               
                   
                 F TAS   
                 Observable not detected because the 
               
               
                   
                   
                 sensor was not tasked. 
               
               
                   
                 F SEN   
                 Observable not detected because the 
               
               
                   
                   
                 sensor or processing did not detect it. 
               
               
                   
                 F ID   
                 Detection processing did not generate 
               
               
                   
                   
                 sufficient ID to support fusion. 
               
               
                   
                 F LOC   
                 Detection and processing did not 
               
               
                   
                   
                 generate a location. 
               
               
                   
                 F DET     —     TIM   
                 Detection and processing did not occur 
               
               
                   
                   
                 in the time allotted (e.g., reported 
               
               
                   
                   
                 too late). 
               
               
                 Fix 
                 F FIX     —     FUS [A/M] 
                 Detection did not fuse due to the 
               
               
                   
                   
                 fusion process (e.g., not everything 
               
               
                   
                   
                 will fuse). 
               
               
                   
                 F FIX     —     FUS     —     TIM [A/M] 
                 Failed to complete fix fusion in the 
               
               
                   
                   
                 time allotted (e.g., fix fused too 
               
               
                   
                   
                 late). 
               
               
                   
                 F FIX [A/M] 
                 Failed fix fusion due to exceeding 
               
               
                   
                   
                 amount of data that can be fused. 
               
               
                 Track 
                 F TRA   
                 Track Failures 
               
               
                 Intent 
                 F INT   
                 Intent Failures 
               
               
                   
               
             
          
         
       
     
         [0136]    Identifying alternatives and/or mitigating  144  may include adding new items to the probabilities sheets (e.g., updating a database storing the probabilities), and then rerunning the simulation modeling program  48  analysis to provide new inputs for repeating analyzing results  140  until required results are obtained. 
         [0137]    Changing available intelligence products and/or varying the environmental conditions  302  a cost-benefit analysis can compare differences between adding and/or removing platforms  260 , sensors  270 , and/or technology. The comparison allows a determination of a best use of assets to combine or utilize alone during different environmental conditions  302 . 
         [0138]    The above description refers to a series of spreadsheets that may be filled manually or automatically by a computer. In an embodiment, an analysis tool may perform one or more of the above steps that automatically and compute results for each performed step. For example, the analysis tool may operate on a computer system to process each step. Alternatively, portions of the analysis tool may operate on separate computers within a computer network comprising a plurality of computers operably connected to one another to process one or more portions of the above steps with the separate computers. 
         [0139]    The above embodiments disclose steps that may be incorporated as instructions on a computer. The instructions may be embodied in various forms like routines, algorithms, modules, methods, threads, or programs including separate applications or code from dynamically or statically linked libraries. Software may also be implemented in a variety of executable or loadable forms including, but not limited to, a stand-alone program, a function call (local or remote), a servlet, an applet, instructions stored in a memory, part of an operating system or other types of executable instructions. It will be appreciated by one of ordinary skill in the art that the form of software may depend, for example, on requirements of a desired application, the environment in which it runs, or the desires of a designer/programmer or the like. It will also be appreciated that computer-readable instructions or executable instructions can be located in one logic or distributed between two or more communicating, co-operating, or parallel processing logics and thus can be loaded or executed in series, parallel, massively parallel and other manners. 
         [0140]    Although the invention has been shown and described with respect to a certain embodiment or embodiments, it is obvious that equivalent alterations and modifications will occur to others skilled in the art upon the reading and understanding of this specification and the annexed drawings. In particular regard to the various functions performed by the above described elements (components, assemblies, devices, compositions, etc.), the terms (including a reference to a “means”) used to describe such elements are intended to correspond, unless otherwise indicated, to any element which performs the specified function of the described element (i.e., that is functionally equivalent), even though not structurally equivalent to the disclosed structure which performs the function in the herein illustrated exemplary embodiment or embodiments of the invention. In addition, while a particular feature of the invention may have been described above with respect to only one or more of several illustrated embodiments, such feature may be combined with one or more other features of the other embodiments, as may be desired and advantageous for any given or particular application.