Synthesized profile

A target identifier can be configured to determine a synthesized profile for a target based on active sensor data that characterizes a radio frequency (“RF”) signal reflected by the target and received at an active sensor system. The synthesized profile can characterize an estimated Line of Bearing (“LoB”) and a radial speed (“Rdot”) of the target relative to a passive sensor system. The target identifier can also be configured to match the synthesized profile with a measured profile that is determined based on RF signals received at the passive sensor system. The measured profile characterizes a measured LoB and a measured Rdot of the target.

TECHNICAL FIELD

This disclosure relates to a synthesized profile of a target.

BACKGROUND

Active radar is an object-detection system that uses radio waves to determine the range, altitude, direction and/or speed of objects. Active radar can be used to detect aircraft, ships, spacecraft, guided missiles, motor vehicles, weather formations and terrain. An active radar system can include a radar dish or antenna that transmits pulses of radio waves or microwaves that bounce off any object in their path. The object reflects a small part of the wave's energy to a dish or antenna that can be located at the same or different site as the transmitter.

Passive sensor systems (also referred to as passive coherent location, passive covert radar and passive radar systems) encompass a class of radar systems that detect and track objects by processing reflections or transmissions from sources of illumination in the environment. Such sources can include, but are not limited to communications signals and commercial broadcast signals that are transmitted or reflected from a target. In some situations, the sources can be cooperative, and in other situations, the sources can be non-cooperative. The term “passive sensor system” can indicate a system that is configured to detect all such sources or some subset thereof.

SUMMARY

One example relates to a target identifier comprising one or more computing devices having machine readable instructions, the target identifier being configured to determine a synthesized profile for a target based on active sensor data that characterizes a radio frequency (“RF”) signal reflected by the target and received at an active sensor system. The synthesized profile can characterize an estimated Line of Bearing (“LoB”) and a radial speed (“Rdot”) of the target relative to a passive sensor system. The target identifier can be further configured to match the synthesized profile with a measured profile that is determined based on RF signals received at the passive sensor system. The measured profile characterizes a measured LoB and a measured Rdot of the target.

Another example relates to a system that can include an active sensor system configured to measure RF signals reflected from a plurality of targets. The reflected RF signals can characterize a range and azimuth for each of the plurality of targets. The system can also include a passive sensor system configured to passively receive RF signals transmitted by the plurality of targets. The system can further include a passive sensor analyzer configured to generate a measured profile for each of the plurality of targets based on the passively received RF signals. Each measured profile can characterize a measured LoB and a measured Rdot over a time period of a corresponding target of the plurality of targets. The system can yet further include an active sensor analyzer configured to determine a position and a track for each of the plurality of targets. The active sensor analyzer can also be configured to determine a synthesized profile for each of the plurality of targets based on the reflected RF signals. The synthesized profile can characterize an estimated LoB and an estimated Rdot over the time period for each of the plurality of targets relative to the passive sensor system. The system can still yet further include a profile matcher configured to match each of the measured profiles with a corresponding synthesized profile.

Yet another example relates to a method that can include determining a measured profile for each of a plurality of targets based on passive measurements, each measured profile can characterize a measured LoB and a measured Rdot over a time period relative to a passive sensor system that that determines the passive measurements. The method can also include generating a synthesized profile for each of the plurality of targets based on data that characterizes RF signals reflected from the plurality of targets. Each synthesized profile can characterize an estimated LoB and an estimated Rdot over the time period for a corresponding target. The method can further include matching each measured profile with a corresponding synthesized profile based on a statistical analysis.

DETAILED DESCRIPTION

Examples of systems and methods for providing identification (ID) and a precise position of targets of interest in an area of interest are described. The system can include a passive sensor platform that can be configured to provide passive sensor data (e.g., measurements) for each of a plurality of targets over a period of time. The passive sensor data can be employed, for example to determine a measured line-of-bearing (“LoB”) and a measured radial speed (“Rdot”), for each of the plurality of targets. The LoB and the Rdot can be employed to determine a target ID based on a signature database. The LoB and the Rdot can be employed to develop (passive) measured profiles for each of the plurality of targets.

The system can also include an active sensor platform configured to provide active sensor data (e.g., measurements of reflected signals) for each of the plurality of targets. The active sensor data can be employed, for example to determine a relatively precise position and a track of each of the plurality of targets over time. The system can employ the active sensor data and kinematics of the passive sensor platform to generate synthesized profiles over the period of time for each of the plurality of targets. Each synthesized profile can include data that provides estimations (predictions) of the LoB and Rdot for an associated target of the plurality of targets that the passive sensor platform should have sensed. The system can include a profile matcher configured to match each measured profile with a corresponding synthesized profile for each of the plurality of targets to generate a combined target data set that includes intelligence gathered from both the active sensor platform and the passive sensor platform.

FIG. 1illustrates a system50for generating synthesized profiles of targets and matching the synthesized profiles to a measured profile. The system can include an area of interest52with N number targets54, where N is an integer greater than or equal to one. Each of the N number of targets54can be an aircraft, a watercraft, a land vehicle, a spacecraft, a guided missile, etc. The system50can include an active sensor system56(platform) and a passive sensor system58(platform). The area of interest52can be, for example, an area that can be tracked by both the active sensor system56and the passive sensor system58.

The active sensor system56can be implemented, for example, as an active radar system. The active sensor system56can be configured to transmit radio frequency (“RF”) signals via an antenna into free space. Each of the N number of targets54that are within range of the active sensor system56(e.g., the area of interest52) reflects a small portion of the RF signal. The reflected portion of the transmitted signal, which can be referred to as a reflected signal, can be received by the active sensor system56at the same or different antenna that propagated the transmitted signal. Data characterizing reflected signals can be provided to a target identifier60, which can be referred to as active sensor data.

The passive sensor system58can operate as a passive radar system. The passive sensor system58can include an antenna configured to detect RF signals transmitted from each of the N number of targets54, which RF signals can be referred to as detected signals. In some examples, the passive sensor data can be employed by the passive sensor system58to determine passive measurements for the N number of targets54. The passive measurements can be provided to the target identifier60. The passive measurements can include, for example, line of bearing (“LoB”) (e.g., an angle of arrival) and radial speed (“Rdot”) relative to the passive sensor system58.

The target identifier60can be implemented, for example, as a computing device, such as a system with a processing unit (e.g., one or more processor cores) as well as memory that can store machine readable instructions. The processing unit can access the memory and execute the machine readable instructions. In other examples, the target identifier60can be implemented as a controller with embedded instructions.

The target identifier60can include a passive sensor analyzer62configured to determine a measured profile for each of the N number of targets54based on the passive measurements. The measured profile of each of the N number of targets54can characterize the LoB over time and the Rdot over time. The passive sensor analyzer62can access a database that includes target signatures to determine a target identification (ID) for each of the N number of targets54based on measured profiles. The target signatures can include, for example, previously determined (measured) waveform parameters, such as a pulse width, a pulse repetition interval, a radio frequency, a scan pattern, etc. The target ID can characterize a type of target54(e.g., a model of an aircraft, a type of guided missile, etc.). The target ID can be added to each measured profile. Additionally, the measured profile for each of the N number of targets54can be provided to a profile matcher64of the target identifier60.

The target identifier60can also include an active sensor analyzer66that can be configured to process the active sensor data that characterizes the reflected signals provided from the active sensor system56. The active sensor analyzer66can be configured to employ the active sensor data to determine a radar range and azimuth measurements of each of the N number of targets54. The determined radar range and azimuth measurements of each of the N number of targets54can be employed to determine a relatively precise location and track for each of the N number of targets54over time. Additionally, the active sensor analyzer66can be configured to receive kinematics characterizing a physical location and velocity of the receiving antenna at the passive sensor system58. The active sensor analyzer66can be configured to employ the kinematics of the passive sensor system58and the active sensor data to determine an estimated (e.g., expected) LoB and Rdot measurements relative to the passive sensor system58. The active sensor analyzer66can generate a synthesized profile of each of the N number of targets54that characterizes the estimated LoB over time and the estimated Rdot over time relative to the passive sensor system58. Additionally, the active sensor analyzer66can add the determined position and tracking of a corresponding target54to each of the synthesized profiles. The synthesized profile for each of the N number of targets54can be provided to the profile matcher64of the target identifier60.

The profile matcher64can employ statistical analysis to match each of the synthesized profiles with a corresponding measured profile. Thus, based on the statistical analysis, the profile matcher64can generate a combined target data set for each of the N number of targets54. The combined target data set can include the relatively accurate position, the track, the target ID, the LoB and the Rdot over time (or some subset thereof) for each of the N number of targets54. Thus, by employment of the system50, intelligence information about each of the N number of targets54determined from the active sensor system56can be combined and reconciled with intelligence information about each of the N number of targets54gathered from the passive sensor system58to generate the combined target data sets, thereby maximizing the potential intelligence for each of the N number of targets54at both the active sensor system56and the passive sensor system58.

FIG. 2illustrates an example of a target identifier100that could be employed, for example, as the target identifier60illustrated inFIG. 1. The target identifier100can include a memory102that can store machine readable instructions and data. The memory102could be implemented, for example, as non-transitory computer readable media, such as volatile memory (e.g., random access memory), nonvolatile memory (e.g., a hard disk drive, a solid state drive, flash memory, etc.) or a combination thereof. The target identifier100can also include a processing unit104to access the memory102and execute the machine-readable instructions. The processing unit104can include, for example, one or more processor cores. The target identifier100can include a network interface106configured to communicate with a network108. The network interface106could be implemented, for example, as a network interface card. The network108could be implemented for example, as a private network (e.g., a local area network (LAN) and/or a wide area network (WAN), a dedicated connection, etc.).

The target identifier100could be implemented, for example in a distributed computing system, such as a computing cloud. In such a situation, features of the target identifier100, such as the processing unit104, the network interface106, and the memory102could be representative of a single instance of hardware or multiple instances of hardware with applications executing across the multiple instances (e.g., distributed) of hardware (e.g., computers, routers, memory, processors, or a combination thereof). For instance, some of the features implemented on the target identifier100could alternatively be implemented in an active sensor system (e.g., the active sensor system56ofFIG. 1) and/or a passive sensor system (e.g., the passive sensor system58ofFIG. 1). Alternatively, the target identifier100could be implemented on a single dedicated computing device.

The target identifier100can include a passive sensor analyzer108that can be configured to process passive measurements110that can be provided to the target identifier100from a passive sensor system (e.g., the passive sensor system58ofFIG. 1) via the network interface106. The passive sensor system can be implemented, for example, as a passive radar system. The passive measurements110can include, for example, an LoB and an Rdot for each of a plurality of targets that are passively detected by the passive sensor system. The target identifier100can also include an active sensor analyzer111configured to process active sensor data113that can be provided from an active sensor system (e.g., the active sensor system56ofFIG. 1) via the network interface106. The active sensor system can be implemented, for example, as an active radar system. The active sensor data113can characterize reflected signals (RF signals) such as portions of transmitted signals that are reflected by the plurality of targets.

The operation of the target identifier100may be better understood with an extended example (hereinafter, “the given example”). In the given example, it is presumed that the active sensor system and the passive sensor system are tracking three targets in an area of interest.FIG. 3is a chart150of X-Y coordinates of an area of interest plotted in arbitrary units of length for the given example. In the chart150, the active sensor system (labeled inFIG. 3as “ACTIVE SENSOR”) is stationary and is positioned at an origin of the chart (position 0,0). Additionally, the passive sensor system (labeled inFIG. 3as “PASSIVE SENSOR”) is moving in the Y direction from about the point (50,0) to about the point (50,110). In the given example, three targets, “Target1”, “Target2” and “Target3” can be detected by both the active sensor system and the passive sensor system. In the given example, each of the three targets could be, for example, aircraft. However, in other examples, the targets could also be terrestrial vehicles and/or watercraft. Moreover, in other examples, there could be more or fewer targets in the area of interest. For the given example, it is presumed that Target1is traveling with a relatively constant heading. Additionally, in the given example, Target2and Target3make 1.5 G constant-speed turns.

In the given example, the passive sensor analyzer108can be configured to determine an LoB and an Rdot for each the three targets over a given time period (e.g., 10 seconds or more) based on the passive measurements110. Additionally, the passive sensor analyzer108can include a profile generator112configured to determine a measured profile for each of the three targets. The measured profile of each of the three targets can include data that characterizes a plot of the LoB over the given time period and a plot that characterizes the Rdot over the given time period for a corresponding target. Continuing with the given example,FIG. 4illustrates an example of a chart200plotting LoB (in knots) as a function of time (in seconds) for Target1, andFIG. 5illustrates an example of a chart210plotting Rdot (in degrees) as a function of time (in seconds) for Target1. Similarly,FIG. 6illustrates an example of a chart220plotting LoB (in knots) as a function of time (in seconds) for Target2, andFIG. 7illustrates an example of a chart230plotting Rdot (in degrees) as a function of time (in seconds) for Target2. Further,FIG. 8illustrates an example of a chart240plotting LoB (in knots) as a function of time (in seconds) for Target3, andFIG. 9illustrates an example of a chart250plotting Rdot (in degrees) as a function of time (in seconds) for Target3.

Referring back toFIG. 2, upon generation of the measured profile, the passive sensor analyzer108can access a profile database114that stores pre-determined waveform signatures for a plurality of identified targets, which waveform signatures can include measured waveform parameters. The database114can be stored locally on the target identifier100, or the database114can be remotely accessible and stored on an external system. The passive sensor analyzer108can employ a pattern matching algorithm to compare the measured waveform parameters of each of the three targets to determine a target ID for each of the three targets. In some examples, the target ID can reveal the type (e.g., the model) of the target. Continuing with the given example, the target ID of each of the three targets can be added to the corresponding measured profile. Additionally, the measured profiles for each of the three targets (Target1, Target2and Target3) can be provided to a profile matcher116of the target identifier100.

Continuing with the given example, the active sensor analyzer111can analyze the active sensor data113to determine a range and azimuth for each of the three targets over the given time period. Moreover, the active sensor can employ the range an azimuth of each of the plurality of targets to determine a position and track of each of the three targets over the given time period.FIG. 10illustrates a chart300that plots the active sensor data113ofFIG. 2for Target3in X-Y coordinates in the given example. As illustrated in the chart300, a series of data points characterizing substantially instantaneous instances of a measured range and azimuth detected as reflected RF signals can be characterized in the active sensor data113. In the given example, the active sensor analyzer111can employ the active sensor data113to determine a position and velocity over time for Target3. The position can be represented as line labeled as “TRACK” inFIG. 10. Referring back toFIG. 2, in the given example, the active sensor system can provide similar active sensor data113for Target1and Target2.

Continuing with the given example, the active sensor analyzer111can include a profile synthesizer118that can generate a synthesized profile that corresponds to an estimated (e.g., expected) LoB and Rdot at the passive sensor system for each of the three targets over the given period of time. To generate the synthesized profiles, the profile synthesizer118can employ kinematics (e.g., position and velocity) of the passive sensor system (e.g., a receiving antenna at the passive sensor system) and the position and track of a corresponding target as generated by the active sensor analyzer111based on the active sensor data113.FIG. 11illustrates an example of a chart320plotting an estimated LoB (in knots) as a function of time (in seconds) for a first target, andFIG. 12illustrates an example of a chart330plotting an estimated Rdot (in degrees) as a function of time (in seconds) for the first target that can be determined by the profile synthesizer118in the given example.FIG. 13illustrates an example of a chart340plotting an estimated LoB (in knots) as a function of time (in seconds) for a second target, andFIG. 14illustrates an example of a chart350plotting an estimated Rdot (in degrees) as a function of time (in seconds) for the second target that can be determined by the profile synthesizer118in the given example.FIG. 15illustrates an example of a chart360plotting an estimated LoB (in knots) as a function of time (in seconds) for a third target, andFIG. 16illustrates an example of a chart370plotting an estimated Rdot (in degrees) as a function of time (in seconds) for the third target that can be determined by the profile synthesizer118in the given example. The active sensor analyzer111can add the position and track of each of the targets to the corresponding synthesized profiles. The synthesized profiles for the first, second and third targets can be provided to the profile matcher116.

The profile matcher116can employ statistical analysis of the measured profiles provided by the passive sensor analyzer108to match each measured profile with a corresponding synthesized profile provided by the active sensor analyzer111. In particular, the profile matcher116can employ statistical analysis to compare each measured LoB and Rdot plot over the given time period for each measured profile to each synthesized LoB and Rdot plot over the given time period for each synthesized profile. The profile matcher116can quantify a match between a given measured profile and a given synthesized profile by calculating a statistical difference or Mahalanobis distance for a given point on of a profile based on Equation 1.
Di2=(mi−si)TSi−1(mi−si)  Equation 1:
wherein:

Additionally, the profile matcher116can employ Equation 2 to determine a cumulative (and normalized) Mahalanobis distance.

D_i2=1i⁢∑k=1i⁢⁢Dk2Equation⁢⁢2
wherein:Di2=The cumulative and normalized Mahalanobis Distance

By employing Equations 1 and 2, the profile matcher116can match each measured profile with a corresponding synthesized profile. In the given example, the cumulative Mahalanobis distance between the synthesized profile for Target1and the measured profiles for the first target, the second target, and the third target is illustrated inFIG. 17. Specifically,FIG. 17illustrates an example of a chart400that plots cumulative (and normalized) Mahalanobis distances as a function of time (in seconds). In the chart400, a first line402characterizes a cumulative and normalized Mahalanobis distance between the synthesized profile for Target1(characterized by the charts inFIGS. 11 and 12) and the measured profile for the third target (characterized in the charts inFIGS. 8 and 9). Additionally, in the chart400, a second line404characterizes a cumulative and normalized Mahalanobis distance between the synthesized profile for Target1(characterized by the charts inFIGS. 11 and 12) and the measured profile for the second target (characterized in the charts inFIGS. 6 and 7). Further, in the chart400, a third line406characterizes a cumulative and normalized Mahalanobis distance between the synthesized profile for Target1(characterized by the charts inFIGS. 11 and 12) and the measured profile for the first target (characterized in the charts inFIGS. 4 and 5). As is illustrated in the chart400, the third line406has the smallest cumulative and normalized Mahalanobis distance over time. Thus, based on the analysis, in the given example, the profile matcher116can match the measured profile of Target1with the synthesized profile of the first target. A similar process can be completed for matching the measured profile of Target2and Target3with the synthesized profiles for the second and third targets.

Additionally, for each point in time recorded, a total cumulative statistical distance can be calculated by the profile matcher116for each possible combination of measured profile and synthesized profile. The pattern matcher can be configured such that the combination that has the smallest statistical distance is selected as the match. The total number of possible combinations is M! (“M-Factorial”), where M is a number of targets. In the given example, the total number of combinations is 3! (6).

To determine the total cumulative Mahalanobis Distance for a particular combination in the given example, the Mahalanobis Distances between the three synthesized-profile-pair-to-actual-profile-pair associations in a combination can be summed. For instance, in Combination1of the given example, there are the following three associations:1.) The synthesized profile pair of Target1is associated with the actual profile pair of Target1;2.) The synthesized profile pair of Target2is associated with the actual profile pair of Target2; and3.) The synthesized profile pair of Target3is associated with the actual profile pair of Target3.

For each of these associations, the following three Mahalanobis Distances between the synthesized profile pair and the actual profile pair can be determined using the method shown and described in Equations 1 and 2:Di1,12=Cumulative and normalized Mahalanobis Distance between the synthesized profile pair of Target1the actual profile pair of Target1at update i;Di2,22=Cumulative and normalized Mahalanobis Distance between the synthesized profile pair of Target2the actual profile pair of Target2at update i; andDi3,32=Cumulative and normalized Mahalanobis Distance between the synthesized profile pair of Target3the actual profile pair of Target3at update i.

The total cumulative Mahalanobis Distance for Combination1,DiC12is given by Equation 3, which is a sum of the cumulative Mahalanobis Distance for each association:
DiC12=Di1,12+Di2,22+Di3,32Equation 3:

In another instance in the given example, consider Combination6, which has the following three associations:1.) The synthesized profile pair of Target1is associated with the actual profile pair of Target3;2.) The synthesized profile pair of Target2is associated with the actual profile pair of Target2; and3.) The synthesized profile pair of Target3is associated with the actual profile pair of Target1.

In the given example, for each of these associations the following three Mahalanobis Distances between the synthesized profile pair and the actual profile pair is determined using the method shown and described in Equations 1 and 2:Di1,32=Cumulative and normalized Mahalanobis Distance between the synthesized profile pair of Target1the actual profile pair of Target1at update i;Di2,22=Cumulative and normalized Mahalanobis Distance between the synthesized profile pair of Target2the actual profile pair of Target2at update i; andDi3,12=Cumulative and normalized Mahalanobis Distance between the synthesized profile pair of Target3the actual profile pair of Target3at update i.

In the given example, the total cumulative Mahalanobis Distance for Combination6,DiC62is given by Equation 4, which is a sum of the cumulative Mahalanobis Distance for each association:
DiC62=Di1,32+Di2,22+Di3,12Equation 4:

FIG. 18illustrates a chart420that plots the total cumulative Mahalanobis DistancesDiC12andDiC62vs. time (in seconds) for Combinations1and6, respectively. Since Combination1is completely correct, Mahalanobis Distance of Combination1,DiC12, is smaller than the Mahalanobis Distance of Combination6,DiC62, over the entire time except the for the beginning (e.g., up to about 50-100 seconds) where the amount of data collected was too small to reveal a significant distinction between Combination1and Combination6.

For the given example,FIG. 19illustrates a chart450plotting of a total cumulative Mahalanobis distance as a function of time (in seconds) for each possible combination of measured profiles and synthesized profiles. In the chart450, each of the six curves (labeled inFIG. 18as “COMBO1” . . . “COMBO6”) are rescaled such that the sum of all six curves as a given time is equal to ‘1’. As is illustrated, combination1has the smallest statistical distance over time. In the given example, combination1corresponds to: the measured profile of Target1being matched with the synthesized profiles of the first target, the measured profile of Target2being matched with the synthesized profiles of the second target and the measured profile of Target3being matched with the synthesized profile of the third target.

By employing the profile matching in the manner described herein, the profile matcher116can generate combined target data sets that characterizes features of the measured profile (a target ID, an LoB and Rdot) with features detected by the active sensor system (e.g., the position and track) (or some subset thereof) of each target in the area of interest. In this manner, inherent limitations of the active sensor system and the passive sensor system can be overcome to provide the combined target data set. That is, intelligence gathered from the passive sensor system can be combined and reconciled with intelligence gathered from the active sensor system. The combined target data set for each target (Target1, Target2and Target3in the given example) can be provided to a graphical user interface (GUI)120. Moreover, the GUI120can employ the combined target data set of each target to generate output for a display.

In view of the foregoing structural and functional features described above, example methods will be better appreciated with reference toFIG. 20. While, for purposes of simplicity of explanation, the example method ofFIG. 20is shown and described as executing serially, it is to be understood and appreciated that the present examples are not limited by the illustrated order, as some actions could in other examples occur in different orders, multiple times and/or concurrently from that shown and described herein. Moreover, it is not necessary that all described actions be performed to implement a method. The example method ofFIG. 19can be implemented as instructions stored in a non-transitory machine-readable medium. The instructions can be accessed by a processing resource (e.g., one or more processor cores) and executed to perform the methods disclosed herein.

FIG. 20illustrates an example flowchart of a method500for associating a measured profile of a target with a synthesized profile of a target. The method500could be implemented, for example, by the system50illustrated inFIG. 1and/or the target verifier100illustrated inFIG. 2. At510, passive sensor data can be received at a passive senor system (e.g., the passive sensor system58ofFIG. 1). The passive sensor data can characterize RF signals that are passively received from K number of targets in an area of interest, where K is an integer greater than or equal to one. At515, the passive sensor system can determine passive measurements that characterize the LoB and Rdot for each of the K number of targets. The passive measurements can be provided to the target identifier. At520, a measured profile of each of the K number of targets can be generated by the target identifier based on the passive measurements. Each measured profile can include a target ID, an LoB over a given period of time and an Rdot over the given period of time for a corresponding target.

At530, active sensor data can be received from an active sensor system (e.g., the active sensor system56ofFIG. 1). The active sensor data can characterize RF signals that are reflected by each of the K number of targets in response to a transmitted signal. At540, a position and track for each of the K number of targets can be determined by the target identifier based on a measured range and azimuth for each of the K number of targets over a given time period that are characterized in the active sensor data.

At550, the target identifier can generate synthesized profiles for each of the K number of targets based on the active sensor data and kinematics (e.g., position and velocity) of the passive sensor system. The synthesized profiles can also include the measured position and track of a corresponding target. At560, the target identifier can perform statistical analysis (e.g., including employing Equations 1 and 2) to match each of the measured profiles for the K number of targets with a corresponding synthesized profile for the K number of targets. For instance, in a manner illustrated and described with respect toFIGS. 17 and 18, Equations 1-4 can be employed to calculate a total statistical distance (e.g., a total cumulative Mahalanobis distance) between each possible combination of the measured profiles and the synthesized profiles to determine a correct combination of measured profiles matched with synthesized profiles. At570, the target identifier can generate a combined target data set for each of the K number of targets based on the matching of the measured and synthesized profiles. Each combined target data set can include a target ID, an LoB over a given time period, an Rdot over the given time period, a position and a track for a corresponding target, or some subset thereof. In this manner, intelligence gathered about the K number of targets from the passive sensor system can be matched and/or reconciled with intelligence gathered from the active sensor system.

In view of the foregoing structural and functional description, those skilled in the art will appreciate that portions of the systems and method disclosed herein may be embodied as a method, data processing system, or computer program product such as a non-transitory computer readable medium. Accordingly, these portions of the approach disclosed herein may take the form of an entirely hardware embodiment, an entirely software embodiment (e.g., in a non-transitory machine readable medium), or an embodiment combining software and hardware. Furthermore, portions of the systems and method disclosed herein may be a computer program product on a computer-usable storage medium having computer readable program code on the medium. Any suitable computer-readable medium may be utilized including, but not limited to, static and dynamic storage devices, hard disks, optical storage devices, and magnetic storage devices.

Certain embodiments have also been described herein with reference to block illustrations of methods, systems, and computer program products. It will be understood that blocks of the illustrations, and combinations of blocks in the illustrations, can be implemented by computer-executable instructions. These computer-executable instructions may be provided to one or more processors of a general purpose computer, special purpose computer, or other programmable data processing apparatus (or a combination of devices and circuits) to produce a machine, such that the instructions, which execute via the one or more processors, implement the functions specified in the block or blocks.