Patent Publication Number: US-7714714-B2

Title: Systems and methods for situational feature set selection for target classification

Description:
BACKGROUND OF THE INVENTION 
   The present invention generally relates to remote sensor systems. More specifically, the present invention relates to systems and methods for improved data communications and/or improved signal processing within remote sensor systems. 
   Throughout the world, military and homeland security forces face an increasing need to provide safety and security to troops and high value assets. Remote sensor systems are ideal for surveillance and monitoring of high-value assets, such as troop encampments, airfields, base installations, supply routes, and depots. In larger networks, remote sensor systems are used to monitor and protect national borders, regional boundaries, and assets in homeland defense and peacekeeping operations. 
   Remote sensor systems typically include a network of easily deployed, remotely located sensors that detect the movement of personnel and vehicles. These sensors are typically remote, battery-operated devices that provide commanders with critical surveillance data on a 24-hour basis. 
   Existing remote sensor systems include several disadvantages. For example, existing remote sensor systems typically flood the network with raw data to be later interpreted at a control station. Additionally, these systems generate a large number of false alarms due to uneventful detections, such as animal movement near the sensors. As another example, existing remote sensor systems are typically designed for a specific application, yet lack sufficient battery life to last the entire mission. 
   Thus, there is a need for systems and methods for improved data communications and/or improved signal processing within remote sensor systems. 
   BRIEF SUMMARY OF THE INVENTION 
   Certain embodiments of the present invention provide a system for improved signal processing within a remote sensor system. The system includes a detection component and a classification component. The detection component is adapted to detect an event. The classification component is adapted to classify the event based at least in part on a situation. 
   Certain embodiments of the present invention provide a method for improved signal processing within a remote sensor system. The system includes determining a situation, detecting an event, and classifying the event based at least in part on a situation. 
   Certain embodiments of the present invention provide a computer readable storage medium. The computer readable storage medium includes a set of instructions for execution on a computer. The set of instructions includes a detection routine and a classification routine. The detection routine is configured to detect an event. The classification routine is configured to classify the event based at least in part on a situation. 

   
     BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS 
       FIG. 1  illustrates an exemplary remote sensor system  100  operating in accordance with an embodiment of the present invention. 
       FIG. 2  illustrates an exemplary remote sensor system  200  operating in accordance with an embodiment of the present invention. 
       FIG. 3  illustrates an exemplary remote sensor system  300  operating in accordance with an embodiment of the present invention. 
       FIG. 4  illustrates a system  400  for improved data communications within a remote sensor system according to an embodiment of the present invention. 
       FIG. 5  illustrates an example  500  of a sensor transmission rule operating in accordance with an embodiment of the present invention. 
       FIG. 6  illustrates an example  600  of a gateway transmission rule operating in accordance with an embodiment of the present invention. 
       FIG. 7  illustrates a method  700  for improved data communications within a remote sensor system according to an embodiment of the present invention. 
       FIG. 8  illustrates a system  800  for improved signal processing within a remote sensor system according to an embodiment of the present invention. 
       FIG. 9  illustrates an exemplary signal processing system  900  operating in accordance with an embodiment of the present invention. 
       FIG. 10  illustrates a method  1000  for improved signal processing within a remote sensor system according to an embodiment of the present invention. 
   

   The foregoing summary, as well as the following detailed description of certain embodiments of the present invention, will be better understood when read in conjunction with the appended drawings. For the purpose of illustrating the invention, certain embodiments are shown in the drawings. It should be understood, however, that the present invention is not limited to the arrangements and instrumentality shown in the attached drawings. 
   DETAILED DESCRIPTION OF THE INVENTION 
     FIG. 1  illustrates an exemplary remote sensor system  100  operating in accordance with an embodiment of the present invention. For example, the system  100  may include a RF-5400 sensor system (Harris Corporation, Rochester, N.Y.). The system  100  includes one or more sensor nodes  110 , a control center node  120 , and one or more communication links  130 . In operation, one or more events  140 , such as vehicles and personnel, may be detected by sensor nodes  110 . For example, sensor node  110  may include a RF-5400VH-SS miniature sensor, a RF-5400VH-MS multiple sensor, and a RF-5400V-SR sensor/relay, as shown in  FIG. 1 . The events  140  may be communicated to control center node  120 , for example, via radio frequency communication links  130 . The control center node  120  may include, for example, a Falcon II handheld radio, also shown in  FIG. 1 . Alternatively and/or in addition, one or more commands (e.g., a command set) may be may be communicated, for example, from the control center node  120  to the sensor node  110 , to provide “on the fly” re-configuration the system  100 . 
     FIG. 2  illustrates an exemplary remote sensor system  200  operating in accordance with an embodiment of the present invention. For example, the system  200  may include a RF-5400 sensor system (Harris Corporation, Rochester, N.Y.). The system  200  includes a plurality of sensor nodes  210 , a plurality of control center nodes  220 , and a plurality of communication links  230 . In operation, one or more events  240 , such as vehicles and personnel, may be detected by sensor nodes  210 . For example, sensor nodes  210  may include a RF-5400VH-SS miniature sensor and an RF-5400V-SR sensor/relay, as shown in  FIG. 2 . The events  240  may be communicated to control center nodes  220 , for example, via radio frequency communication links  230 . The control center nodes  220  may include, for example, a Falcon II handheld radio, a RF-5800V-MP manpack radio, a RF-5410 sensor management application, and a RF-6910 situational awareness application, also shown in  FIG. 2 . Alternatively and/or in addition, one or more commands (e.g., a command set) may be may be communicated, for example, from the control center nodes  220  to the sensor nodes  210 , to provide “on the fly” re-configuration the system  200 . 
     FIG. 3  illustrates an exemplary remote sensor system  300  operating in accordance with an embodiment of the present invention. For example, the system  300  may include a RF-5400 sensor system (Harris Corporation, Rochester, N.Y.). The system  300  includes a plurality of sensor nodes  310 , a plurality of gateway nodes  315 , a control center node  320 , and a plurality of communication links  330 . In operation, one or more events  340 , such as vehicles and personnel, may be detected by sensor nodes  310 . For example, sensor nodes  310  may include a RF-5400VH-SS miniature sensor and a RF-5400VH-MS multiple sensor, as shown in  FIG. 3 . The events  340  may be communicated to gateway nodes  315 , for example, via radio frequency communication links  330 . The gateway nodes  315  may include, for example, a RF-5400VH-RU relay and a RF-5400VH-GW intelligent gateway, also shown in  FIG. 3 . The events  340  may be communicated to control center node  320 , for example, via satellite communication links  330 . The control center node  320  may include, for example, a RF-5410 sensor management application and a RF-6910 situational awareness application, also shown in  FIG. 3 . Alternatively and/or in addition, one or more commands (e.g., a command set) may be may be communicated, for example, from the control center node  320  to the gateway nodes  315  and/or the sensor nodes  310 , to provide “on the fly” re-configuration the system  300 . 
     FIG. 4  illustrates a system  400  for improved data communications within a remote sensor system according to an embodiment of the present invention. For example, the system  400  may include a RF-5400 sensor system (Harris Corporation, Rochester, N.Y.). The system  400  includes a sensor layer  410 , a gateway layer  420 , and control center layer  430 , which are described in more detail below. 
   The sensor layer  410  may include one or more sensor nodes  415 . For example, the sensor nodes  415  may include sensors, such as RF-5400VH-SS miniature sensors, RF-5400VH-MS multiple sensors, and/or RF-5400V-SR sensors/relays. The sensor nodes  415  may also include, for example, detectors, such as seismic, acoustic, magnetic, and/or passive infra-red (PIR) detectors. 
   The gateway layer  420  may include one or more gateway nodes  425 . For example, the gateway nodes  425  may include range extenders, such as RF-5400VH-RU relays, RF-5400V-SR sensor/relays, and/or RF-5400VH-GW intelligent gateways. 
   The control center layer  430  may include one or more control center nodes  435 . For example, the control center node  435  may include monitors, such as Falcon II handheld radios, RF-5800V-MP manpack radios, RF-5410 sensor management applications, and/or RF-6910 situational awareness applications. 
   The sensor layer  410  is in communication with the gateway layer  420  and/or the control center layer  430 . For example, as shown in  FIG. 4 , a plurality of sensor nodes  415  may communicate directly with a gateway node  425  and indirectly with a control center node  435 . As another example, the plurality of sensor nodes  415  may communicate directly with the control center node  435 . 
   The gateway layer  420  is in communication with the sensor layer  410  and/or the control center layer  430 . For example, as shown in  FIG. 4 , a gateway node  425  may communicate with a plurality of sensor nodes  415  and a plurality of gateway nodes  425  may communicate with a control center node  435 . 
   The control center layer  430  is in communication with the sensor layer  410  and/or the gateway layer  420 . For example, as shown in  FIG. 4 , a control center node  435  may communicate directly with a plurality of gateway nodes  425  and indirectly with a plurality of sensor nodes  415 . 
   The sensor layer  410  is adapted to detect one or more events. For example, a seismic detector in sensor node  415  may be adapted to detect movement of personnel. As another example, a passive infra-red (PIR) detector in sensor node  410  may be adapted to detect left to right movement of vehicles. 
   The sensor layer  410  is adapted to generate data based at least in part on one or more events. The data may include, for example, data, signals, events, and/or reports. The data may be stored, for example, in a database. The database may be indexed, for example, based at least in part on network identification (network ID), cluster area, time, classification, direction, global positioning satellite location (GPS location), and/or detection type. 
   The sensor layer  410  is adapted to process and/or communicate data based at least in part on more or more rules and/or algorithms (e.g., a rule or algorithm set). For example, a plurality of sensor nodes  415  may be adapted to transmit data to a gateway node  425  and/or a control center node  435  based at least in part on a sensor transmission rule. The rule set may include, for example, ordered/unordered events, direction specific events, and/or classification specific events. The rule set may be configured “on the fly”, for example, by the system  400  and/or a user of the system  400 . The rule set may be configured remotely, for example, from any node in the system  400 . 
     FIG. 5  illustrates an example  500  of a sensor transmission rule operating in accordance with an embodiment of the present invention. The example  500  includes a data transmission sequence  510  and a data hold/drop sequence  520 . The data transmission sequence  510  includes a sliding association window  515 . The example  500  also includes data  530 , which may include, for example, data, signals, events, and/or reports. The example  500  is described with reference to the system  400  of  FIG. 4 , but it should be understood that other implementations are possible. 
   In operation, the data  530  is compared to the sensor transmission rule. If the rule is satisfied, then the data  530  may be communicated. Alternatively and/or in addition, a single event corresponding to the rule may be transmitted to reduce the amount of data transmitted and still indicate that the rule has been satisfied. Conversely, if the rule is not satisfied, then the data  530  may be held and/or dropped. 
   For example, RULE X may be configured by a user to be a combination of EVENT A, EVENT B, and EVENT C in the order of A&gt;C&gt;B within 60 seconds, where EVENT A includes passive infra-red (PIR) detection of a relative left to right moving target, EVENT B includes seismic detection of any classification, and EVENT C includes magnetic “tripwire” detection. The data  530  in the sliding association window  515  of the data transmission sequence  510  satisfies RULE X, and consequently, may be transmitted, for example, from a sensor node  415  to a gateway node  425  and/or a control center node  435 . Alternatively and/or in addition, a single event corresponding to RULE X may be transmitted to reduce the amount of data transmitted and still indicate that RULE X has been satisfied. Conversely, the data  530  in the data hold/drop sequence  520  does not satisfy RULE X, and consequently, may be held and/or dropped. 
   Although the data transmission sequence  510  is described with reference to a single sliding association rule  515 , it should be understood that one or more sliding association windows  515  are possible. For example, if a second start condition is present, such as EVENT A, then a second sliding association window  515  may be started. If EVENT C and then EVENT B follow within 60 seconds of EVENT A, then RULE X is satisfied and the data  530  in the second sliding association window  515  may be transmitted, for example, from the sensor node  415  of  FIG. 4  to a gateway node  425  and/or a control center node  435 . Conversely, if EVENT C and then EVENT B do not follow within 60 seconds of EVENT A, then RULE X is not satisfied, the second sliding window  515  may be closed, and the data  530  in the second sliding association window  515  may be held and/or dropped. 
   The gateway layer  420  is adapted to process and/or communicate data based at least in part on one or more rules and/or algorithms (e.g., a rule or algorithm set). For example, a gateway node  425  may be adapted to receive data from one or more sensor nodes  415  and transmit the data to a control center node  435  based at least in part on a gateway transmission rule. The rule set may include, for example, ordered/unordered events, node identification (node ID) based events, and/or report type based events. The rule set may be configured “on the fly”, for example, by the system  400  and/or a user of the system  400 . The rule set may be configured remotely, for example, from any node in the system  400 . 
     FIG. 6  illustrates an example  600  of a gateway layer rule operating in accordance with an embodiment of the present invention. The example  600  includes a data transmission sequence  610  and a data hold/drop sequence  620 . The data transmission sequence  610  includes a sliding association window  615 . The example  600  also includes data  630 , which may include, for example, data, signals, events, and/or reports. 
   In operation, the data  630  is compared to the sensor layer rule. If the rule is satisfied, then the data  630  may be transmitted. Alternatively and/or in addition, a single event corresponding to the rule may be transmitted to reduce the amount of data transmitted and still indicate that the rule has been satisfied. Conversely, if the rule is not satisfied, then the data  630  may be held and/or dropped. 
   For example, RULE Y may be configured by a user to include a combination of SENSOR NODE  21 , SENSOR NODE  22 , and SENSOR NODE  23 , each sensor node reporting identical events in the order  21 &gt; 22 &gt; 23  within 180 seconds. The data  630  in the sliding association window  615  of the data transmission sequence  610  satisfies RULE Y, and consequently, may be transmitted, for example, from a gateway node  425  to a control center node  435 . Alternatively, a single event corresponding to RULE Y may be transmitted to reduce the amount of data transmitted and still indicate that RULE Y has been satisfied. Conversely, the data  630  in the data hold/drop sequence  620  does not satisfy RULE Y, and consequently, may be held and/or dropped. 
   Although the data transmission sequence  610  is described with reference to a single sliding association rule  615 , it should be understood that one or more sliding association windows  615  are possible. For example, if a second start condition is present, such as SENSOR NODE  21  reporting an event, then a second sliding association window  615  may be started. If SENSOR NODE  22  and then SENSOR NODE  23  report the same event within 180 seconds, then RULE Y is satisfied, and consequently, the data  630  in the second sliding association window  615  may be transmitted, for example, from a gateway node  425  to a control center node  435 . Conversely, if SENSOR NODE  22  and then SENSOR NODE  23  do not report the same event within 180 seconds, then RULE Y is not satisfied, the second sliding association window  615  may be closed, and the data  630  in the second sliding association widow  615  may be held and/or dropped. 
   The control center layer  430  is adapted to notify a user based at least in part on one or more rules and/or algorithms (e.g., a rule or algorithm set). For example, a control center node  435  may receive data from a plurality of sensor nodes  415  and/or a plurality of gateway nodes  425  and automatically alert a user when the data matches a pattern recognition template. The rule set may include, for example, ordered/unordered events and/or analog/digital signatures. The rule set may be implemented using a sliding association window, as described above. The rule set may be configured “on the fly”, for example, by the system  400  and/or a user of the system  400 . The rule set may be configured remotely, for example, from any node in the system  400 . 
   For example, a user may create a pattern recognition template that matches on relative left to right movement of a vehicle reported first from SENSOR NODE A and then from SENSOR NODE B within 5 minutes. The control center layer  430  may query a database and alert the user when the data in the database matches the pattern recognition template. 
   The rule set may be run continuously or periodically (e.g., daily, hourly, etc.) depending on the particular application of the system  400 , such as force protection, perimeter surveillance, and/or remote boarder monitoring. 
   As discussed above, the components, elements, and/or functionality of the system  400  may be implemented alone or in combination in various forms in hardware, firmware, and/or as a set of instructions in software, for example. Certain embodiments may be provided as a set of instructions residing on a computer-readable medium, such as a memory, hard disk, DVD, or CD, for execution on a general purpose computer or other processing device. 
     FIG. 7  illustrates a method  700  for improved data communications within a remote sensor system according to an embodiment of the present invention. The method  700  includes the following steps, which are described in more detail below. At step  710 , an event is detected. At step  720 , data is generated. At step  730 , the data is processed. At step  740 , the data is communicated. At step  750 , a user is notified. The method  700  is described with reference to the components of the system  400  of  FIG. 4 , but it should be understood that other implementations are possible. 
   At step  710 , an event is detected, for example, by a sensor node, such as sensor node  415 . The event may include, for example, left to right vehicle movement detected by a passive infra-red (PIR) detector. 
   At step  720 , data is generated based at least in part on the event. For example, the left to right vehicle movement detected by the passive infra-red (PIR) detector may be reported in a database. 
   At step  730 , the data is processed based at least in part on a rule. For example, if the rule is satisfied (e.g., the data matches the rule), then the data may be transmitted. Conversely, for example, if the rule is not satisfied (e.g., the data does not match the rule), then the data may be held and/or dropped. The rule may include, for example, a sensor transmission rule, a gateway transmission rule, and/or a control center notification rule. 
   At step  740 , the data is communicated. For example, the data may be transmitted by the sensor node and received by a gateway node, such as gateway node  425 , and/or a control center node, such as control center node  435 . As another example, the data may be transmitted by the gateway node and received by the control center node. 
   In certain embodiments of the present invention, the data may be communicated when the rule is satisfied. For example, the data may be transmitted from the sensor node to the gateway node and/or the control center node when a sensor transmission rule is satisfied. As another example, the data may be transmitted from the gateway node to the control center node when a gateway rule is satisfied. 
   At step  750 , a user is notified when the control center notification rule is satisfied. For example, a user may be automatically alerted when data in a database matches a pattern recognition template. 
   One or more of the steps  710 - 750  of the method  700  may be implemented alone or in combination in hardware, firmware, and/or as a set of instructions in software, for example. Certain embodiments may be provided as a set of instructions residing on a computer-readable medium, such as a memory, hard disk, DVD, or CD, for execution on a general purpose computer or other processing device. 
   Certain embodiments of the present invention may omit one or more of these steps and/or perform the steps in a different order than the order listed. For example, some steps may not be performed in certain embodiments of the present invention. As a further example, certain steps may be performed in a different temporal order, including simultaneously, than listed above. 
     FIG. 8  illustrates a signal processing system  800  according to an embodiment of the present invention. For example, the system  800  may be implemented as part of a sensor system, such as the sensor system  400  of  FIG. 4 . The system  800  includes a detection component  810 , a processing component  820 , and analysis component  830 , a classification component  840 , and a communication component  850 . The system  800  is described with reference to the components of the sensor system  400  of  FIG. 4 , but it should be understood that other implementations are possible. 
   The detection component  810  is adapted to detect one or more events, such as movement of personnel and/or vehicles. The detection component  810  may include detectors, such as seismic, acoustic, magnetic, and/or passive infra-red (PIR) detectors. For example, a seismic detector in a sensor node, such as sensor node  415  of  FIG. 4 , may communicate a signal that can be later interpreted to be personnel and/or vehicles. As another example, a passive infra-red (PIR) detector in a sensor node, such as sensor node  415  of  FIG. 4 , may communicate a signal that can be later interpreted to be directional and/or tripline. 
   The detection component  810  is adapted to generate a signal based at least in part on the event. For example, a seismic detector may detect seismic vibrations and generate a corresponding electrical signal. As another example, a passive infra-red (PIR) detector may detect infra-red energy and generate a corresponding electrical signal. 
   The processing component  820  is adapted to process a signal. For example, the processing component  820  may be adapted to process the signal generated by the detection component  810 . 
   In certain embodiments of the present invention, the processing component  820  may be adapted to process the signal based at least in part on a situation.  FIG. 9  illustrates an exemplary signal processing system  900  operating in accordance with an embodiment of the present invention. As shown in  FIG. 9 , for example, the signal may be processed by a bandpass filter component  901  and/or stored in a signal buffer  902  based at least in part on the situation. That is, processing parameters, such as filter bandwidth, may be determined based at least in part on the situation. Table 1, as provided below, includes several examples of situations and corresponding processing parameters. 
   
     
       
         
             
           
             
               TABLE 1 
             
           
          
             
                 
             
             
               Examples of Situations and Corresponding Parameters 
             
          
         
         
             
             
             
          
             
                 
               Processing/Analysis 
                 
             
             
               Situation 
               Prameters 
               Classification Parameters 
             
             
                 
             
             
               Environment: Rain 
               Filter taps 
               N/A 
             
             
                 
               Detection Threshold 
             
             
               Environment: 
               Filter taps 
               Envelope frequency 
             
             
               Smooth road 
               Detection Threshold 
               veh freq mask A 
             
             
               Target: Pedestrian 
               Detection Pulse length 
               Ped gait frequency test 
             
             
               and Vehicle 
               Detection Pulse spacing 
               Time series of envelope 
             
             
                 
                 
               Input Freq 
             
             
                 
                 
               veh freq mask B 
             
             
               Target: Pedestrian 
               Filter taps 
               Envelope frequency 
             
             
                 
               Detection Threshold 
               Ped gait frequency test 
             
             
                 
               Detection Pulse length 
               Time series of envelope 
             
             
                 
               Detection Pulse spacing 
               Kurtosis 
             
             
               Environment: 
               Filter taps 
               Envelope frequency 
             
             
               Rough road 
               Detection Threshold 
               veh freq mask C 
             
             
               Target: vehicle 
               Detection Pulse length 
               Input Freq 
             
             
                 
                 
               veh freq mask D 
             
             
                 
             
          
         
       
     
   
   The situation may include, for example, a target type, such as an aircraft, a vehicle, a pedestrian, and/or an animal. As another example, the situation may include an environment or an environmental condition, such as a surface (e.g., smooth, rough, dirt, gravel, paved, loose, packed, etc.) and/or a surrounding area (e.g., plains, forests, hills, mountains, etc.). As another example, the situation may include a dynamic environment or a dynamic environmental condition, such as a weather condition (e.g., rain, snow, wind, etc.) and/or a military activity (e.g., heavy artillery fire, bombardment, etc.). 
   In certain embodiments of the present invention, the situation may be determined, for example, by a user. For example, a user may input a situation using an input device, such as a keyboard, a mouse, and/or a touchscreen. As another example, a user may select a situation from a plurality of available situations, for example, by selecting the situation from a drop- or pull-down menu using a mouse and/or selecting a checkbox associated with the situation on a touchscreen. Alternatively and/or in addition, the situation may be determined automatically, for example, by the system  800 . 
   Thus, certain embodiments of the present invention provide systems and methods for adaptive and/or dynamic signal processing. That is, a signal may be processed, for example, based at least in part on a situation, as described above. An example of adaptive signal processing is provided below. 
   EXAMPLE 
   Adaptive Signal Processing 
   Situation: Searching for vehicles 
   Environment: Paved road through a wooded area 
   Dynamic Environmental Condition: Rain 
   When the rain begins, seismic detectors receive large interfering impulses. The system  800  detects the environmental change and adapts the input bandpass filter to notch out those components, resulting in a “cleaner” signal (e.g., better signal to noise and interference ratio), and in turn, improved detection and classification probability. When the rain stops, the adaptive front-end filter returns to the normal frequency response. 
   In certain embodiments of the present invention, the processing component  830  may be adapted to determine the signal envelope based at least in part on the situation. For example, as shown in  FIG. 9 , the signal envelope may be determined by a magnitude component  903  and a filter and decimate component  904  and stored in a signal envelope buffer component  905  based at least in part on the situation. That is, processing parameters, such as filter bandwidth, may be determined based at least in part on the situation. Table 1, as provided above, includes several examples of situations and corresponding processing parameters. 
   In certain embodiments of the present invention, the processing component  820  may be adapted to process and/or analyze the signal envelope based at least in part on the situation. For example, as shown in  FIG. 9 , the signal envelope may be processed by the mean and standard deviation component  906 , the average component  906 , the calculation threshold component  908 , the comparison component  909 , and the association window component  910  based at least in part on the situation. That is, processing/analysis parameters, such as detection threshold, detection pulse length, detection pulse spacing, detection time window length, number of pulses in the detection time window, association window length, and/or association window N of M threshold, may be determined based at least in part on the situation. Table 1, as provided above, includes several examples of situations and corresponding processing/analysis parameters. 
   The analysis component  830  is adapted to analyze a signal. For example, the analysis component  830  may be adapted to analyze the signal generated by the detection component  810 . As another example, the analysis component  830  may be adapted to analyze the signal processed by the processing component  820 . 
   In certain embodiments of the present invention, the analysis component  820  may be adapted to analyze the signal, for example, in one or more domains. For example, as shown in  FIG. 9 , the signal stored in the signal buffer  902  may be analyzed in the frequency domain (e.g., ordered ranking of peaks, frequency sub-band sums, desired fundamental and harmonic association filters) by the frequency analysis component  914 . As another example, the signal may be analyzed in the time domain (e.g., kurtosis, cadence analysis) by the time analysis component  915 . 
   In certain embodiments of the present invention, the analysis component  830  may be adapted to analyze the signal envelope, for example, in one or more domains. For example, as shown in  FIG. 9 , the signal envelope stored in the signal envelope buffer  905  may be analyzed in the frequency domain (e.g., ordered ranking of peaks, frequency sub-band sums, desired fundamental and harmonic association filters) by the frequency analysis component  912 . As another example, the signal envelope may be analyzed in the time domain (e.g., kurtosis, cadence analysis) by the time analysis component  913 . 
   In certain embodiments of the present invention, the analysis component  830  may be adapted to process and/or analyze the signal envelope based at least in part on the situation. For example, as shown in  FIG. 9 , the signal envelope may be analyzed by the pulse analysis component  911  based at least in part on the situation. That is, processing/analysis parameters, such as detection threshold, detection pulse length, detection pulse spacing, detection time window length, number of pulses in the detection time window, association window length, and/or association window N of M threshold, may be based at least in part on the situation. Table 1, as provided above, includes several examples of situations and corresponding processing/analysis parameters. 
   Thus, certain embodiments of the present invention provide systems and methods for envelope signal processing. That is, the envelope of a signal may be processed and/or analyzed, for example, based at least in part on the situation, as described above. An example of envelope signal processing is provided below. 
   EXAMPLE 
   Envelope Signal Processing 
   For a seismic signal, the comparison between the low frequency components and the rest of the frequency components of the envelope can be used as a feature to help determine the difference between impulse sources such as a person or animal and constant sources such as a vehicle. Using the envelope of the signal allows for lower sampling rates than processing the original signal. This in turn reduces memory, processor loading, and power requirements. These comparisons include but are not limited to ratio between the low frequency components and the rest of the frequency components, the power in the low frequency components and the total signal power, and/or the difference between select groups of frequency components. 
   The classification component  840  is adapted to classify an event. For example, the event may be classified as a pedestrian, a vehicle, a light vehicle, a heavy vehicle, a wheeled vehicle, a tracked vehicle, and/or another appropriate classification. The classification may include data, signals, events, and/or reports. 
   In certain embodiments of the present invention, the classification component  840  may be adapted to classify the event based at least in part on a situation. For example, as shown in  FIG. 9 , a classification component  916  may be adapted to actuate one or more switches  917  based at least in part on the situation. That is, classification parameters, such as feature selection and/or feature execution order, may be based at least in part on the situation. Table 1, as provided above, includes several examples of situations and corresponding classification parameters. 
   Thus, certain embodiments of the present invention provide systems and methods for multi-domain signal processing. That is, a signal and/or a signal envelope may be analyzed, for example, in more than one domain. An example of multi-domain signal processing is provided below. 
   EXAMPLE 
   Multi-Domain Signal Processing 
   Use of features from different domains to classify a target 
   Situation: Searching for pedestrian and vehicles 
   Environment: Packed soil 
   Features used: 
   Domain: Frequency spectrum of the envelope 
   Feature: Existence of a peak at the gait rate of a person&#39;s walk and the first harmonic. 
   Reason: This feature will detect the presence of impulses that occur at the rate of a person&#39;s feet impact with the ground. 
   Domain: time series of the envelope 
   Feature: Kurtosis 
   Reason: This feature measures the “peakedness” of the probability distribution. This measurement will be different for a vehicle verses a pedestrian. 
   Domain: Frequency domain of the input digital samples 
   Feature: High frequency band power 
   Reason: This feature measures the presence of a frequency source that is typically higher then the resonant frequency provide from the impact of a person&#39;s step. 
   By themselves these features are not reliable enough to accurately predict the presence of a person verses a vehicle. However, when the expected results of these features are combined, a better estimate of the target is achieved. By using features from different domains, the interdependency between the features measured is much smaller then using all features from the same domain. 
   Additionally and/or alternatively, certain embodiments of the present invention provide systems and methods for power efficient signal processing. That is, a signal and/or a signal envelope may be processed, analyzed, and/or classified based at least in part on a limited set of features. An example of power efficient signal processing is provided below. 
   EXAMPLE 
   Power Efficient Signal Processing 
   Not using all the features all the time. 
   Situation: Searching for pedestrian 
   Environment: Packed soil 
   Features used: 
   Domain: Frequency spectrum of the envelope 
   Feature: Existence of a peak at the gait rate of a person&#39;s walk and the first harmonic. 
   Reason: This feature will detect the presence of impulses that occur at the rate of a person&#39;s feet impact with the ground. 
   Domain: time series of the envelope 
   Feature: Kurtosis 
   Reason: This feature measures the “peakedness” of the probability distribution. This measurement will be different for background noise and other man made sources verses a pedestrian walking. 
   When searching for a pedestrian against background noise, only the gait of the footstep and the Kurtosis need to be used. If the gait rate is found along with a harmonic to that rate, there is a high confidence that it is a person that is walking past the sensor. A detection of a pedestrian is declared. If the values for the gait fundamental and harmonic are not sufficient for a confident decision, the Kurtosis is calculated. If the value of the Kurtosis combined with the gait frequency is sufficient, a pedestrian is declared. Otherwise it is assumed that the source was not a person. 
   Based on the confidence level of the classification from one or multiple of the features, the signal processing can either continue evaluating features or stop and report the classification. This reduces the processing requirements as well as the overall average system classification delay for resource limited remote sensors. 
   Additionally and/or alternatively, certain embodiments of the present invention provide systems and methods for multi-observation signal processing. That is, more than one signal may be processed, analyzed, and/or classified, as described above. An example of multi-observation signal processing is provided below. 
   EXAMPLE 
   Multi-Observation Signal Processing 
   Using multiple observations of the same features to classify a target. 
   Situation: Searching for pedestrian and vehicles 
   Environment: Packed soil 
   Features used: 
   Domain: Frequency spectrum of the envelope 
   Feature: Existence of a peak at the gait rate of a person&#39;s walk and the first harmonic. 
   Reason: This feature will detect the presence of impulses that occur at the rate of a person&#39;s feet impact with the ground. 
   Domain: time series of the envelope 
   Feature: Kurtosis 
   Reason: This feature measures the “peakedness” of the probability distribution. This measurement will be different for a vehicle verses a pedestrian. 
   Domain: Frequency domain of the input digital samples 
   Feature: High frequency band power 
   Reason: This feature measures the presence of a frequency source that is typically higher then the resonant frequency provide from the impact of a person&#39;s step. 
   The classification results of the features calculated and a probability of a pedestrian and vehicle are estimated. If the probability of either the pedestrian or the vehicle is not sufficient to make a decision, the same features are calculated again after a given delay in time. These probabilities are combined with the first observation time instance. This is repeated until either, the signal goes away or the probability threshold (or simple voting) of a pedestrian or vehicle is exceed. 
   The system performs a classification decisions based on but not limited to one feature set or a multiple of feature sets. The decision can be a hard decision for one of the possible classification hypotheses or a soft decision where a weight or probability is assigned to each possible decision hypothesis. The classification decision hypotheses are given a weight value. This weight value can be but not limited to a measure of the signal to noise ratio, confidence level, signal energy, or a simple value of one or zero. Over multiple observation periods, the weighted classification decisions are combined for the possible decision hypotheses. The combination of weighted decision values over different observation windows can be for a fixed duration of observation windows, until a difference threshold between classification hypotheses is achieved, or until the target exits the sensor range limits. 
   The communication component  850  is adapted to communicate a classification. For example, the communication component  850  may be adapted to transmit a classification data and/or a classification report from a sensor node, such as sensor node  415  of  FIG. 4 , to a gateway node, such as gateway node  425  of  FIG. 4 , and/or a control center node, such as control center node  435  of  FIG. 4 . 
   In certain embodiments of the present invention, the communication component  850  may be adapted to communicate with and/or facilitate communications between the other components  810 - 840  of the system  800 . For example, the detection component  810  may communicate directly with the processing component  820 . As another example, the detection component  810  may communicate indirectly with the processing component  820  via the communication component  850 . That is, all of the components  810 - 850  of the system  800  may communicate directly with each other and/or indirectly via the communication component  850 , as described above. 
   As discussed above, the components, elements, and/or functionality of the system  800  may be implemented alone or in combination in various forms in hardware, firmware, and/or as a set of instructions in software, for example. Certain embodiments may be provided as a set of instructions residing on a computer-readable medium, such as a memory, hard disk, DVD, or CD, for execution on a general purpose computer or other processing device. 
     FIG. 10  illustrates a method  1000  for improved signal processing within a remote sensor system according to an embodiment of the present invention. At step  1010 , a situation is determined. At step  1020 , an event is detected. At step  1030 , a signal is generated. At step  1040 , the signal is processed. At step  1050 , the signal is analyzed. At step  1060 , the event is classified. At step  1070 , the event is communicated. The method  1000  includes the following steps, which are discussed in more detail below. The method  1000  is described with respect to the system  800 , but it should be understood that other implementations are possible. 
   At step  1010 , a situation is determined. For example, the situation may include a target type, an environment or environmental condition, and/or a dynamic environment or a dynamic environmental condition. The situation may be determined, for example, by the system  800  and/or by a user, as described above. 
   At step  1020 , an event is detected, for example, by a detection component, such as detection component  810 . For example, a vehicle may be detected by a seismic detector. At step  1030 , a signal is generated by the detection component based at least in part on the event. For example, the seismic detector may generate an electrical signal from the seismic vibrations of the vehicle. 
   At step  1040 , the signal is processed, for example, by a processing component, such as processing component  820 . The signal may include, for example, the signal generated at step  1030 . In certain embodiments of the present invention, the signal may be processed based at least in part on the situation. In certain embodiments of the present invention, the signal may be processed to determine a signal envelope. The signal envelope may be further processed based at least in part on the situation. 
   At step  1050 , the signal is analyzed, for example, by an analysis component, such as analysis component  830 . The signal may include, for example, the signal generated at step  1030  and/or the signal processed at step  1040 . In certain embodiments of the present invention, the signal and/or the signal envelope may be analyzed, for example, in one or more domains. In certain embodiments of the present invention, the signal envelope may be analyzed based at least in part on the situation. 
   At step  1060 , an event is classified, for example, by a classification component, such as classification component  840 . For example, an impulse source may be classified as a person, whereas a constant source may be classified as a vehicle. 
   At step  1070 , the classification is communicated, for example, by a communication component, such as communication component  850 . 
   One or more of the steps  1010 - 1070  of the method  1000  may be implemented alone or in combination in hardware, firmware, and/or as a set of instructions in software, for example. Certain embodiments may be provided as a set of instructions residing on a computer-readable medium, such as a memory, hard disk, DVD, or CD, for execution on a general purpose computer or other processing device. 
   Certain embodiments of the present invention may omit one or more of these steps and/or perform the steps in a different order than the order listed. For example, some steps may not be performed in certain embodiments of the present invention. As a further example, certain steps may be performed in a different temporal order, including simultaneously, than listed above. 
   In one embodiment of the present invention, a system for improved signal processing within a remote sensor system includes a detection component and a classification component. The detection component is adapted to detect an event. The classification component is adapted to classify the event based at least in part on a situation. 
   In one embodiment of the present invention, a method for improved signal processing within a remote sensor system includes determining a situation, detecting an event, and classifying the event based at least in part on a situation. 
   In one embodiment of the present invention, a computer readable storage medium includes a set of instructions for execution on a computer. The set of instructions includes a detection routine and a classification routine. The detection routine is configured to detect an event. The classification routine is configured to classify the event based at least in part on a situation. 
   Thus, certain embodiments of the present invention provide systems and methods for improved data communication and/or improved signal processing within a remote sensor system. Certain embodiments of the present invention provide the technical effect of improved data communication and/or improved signal processing within a remote sensor system. 
   Several embodiments are described above with reference to drawings. These drawings illustrate certain details of specific embodiments that implement the systems and methods and programs of the present invention. However, describing the invention with drawings should not be construed as imposing on the invention any limitations associated with features shown in the drawings. The present invention contemplates methods, systems and program products on any machine-readable media for accomplishing its operations. As noted above, the embodiments of the present invention may be implemented using an existing computer processor, or by a special purpose computer processor incorporated for this or another purpose or by a hardwired system. 
   As noted above, embodiments within the scope of the present invention include program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available media that can be accessed by a general purpose or special purpose computer or other machine with a processor. By way of example, such machine-readable media may comprise RAM, ROM, PROM, EPROM, EEPROM, Flash, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer or other machine with a processor. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a machine, the machine properly views the connection as a machine-readable medium. Thus, any such a connection is properly termed a machine-readable medium. Combinations of the above are also included within the scope of machine-readable media. Machine-executable instructions comprise, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing machines to perform a certain function or group of functions. 
   Embodiments of the invention are described in the general context of method steps which may be implemented in one embodiment by a program product including machine-executable instructions, such as program code, for example in the form of program modules executed by machines in networked environments. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Machine-executable instructions, associated data structures, and program modules represent examples of program code for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represent examples of corresponding acts for implementing the functions described in such steps. 
   Embodiments of the present invention may be practiced in a networked environment using logical connections to one or more remote computers having processors. Logical connections may include a local area network (LAN) and a wide area network (WAN) that are presented here by way of example and not limitation. Such networking environments are commonplace in office-wide or enterprise-wide computer networks, intranets and the Internet and may use a wide variety of different communication protocols. Those skilled in the art will appreciate that such network computing environments will typically encompass many types of computer system configurations, including personal computers, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like. Embodiments of the invention may also be practiced in distributed computing environments where tasks are performed by local and remote processing devices that are linked (either by hardwired links, wireless links, or by a combination of hardwired or wireless links) through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices. 
   An exemplary system for implementing the overall system or portions of the invention might include a general purpose computing device in the form of a computer, including a processing unit, a system memory, and a system bus that couples various system components including the system memory to the processing unit. The system memory may include read only memory (ROM) and random access memory (RAM). The computer may also include a magnetic hard disk drive for reading from and writing to a magnetic hard disk, a magnetic disk drive for reading from or writing to a removable magnetic disk, and an optical disk drive for reading from or writing to a removable optical disk such as a CD ROM or other optical media. The drives and their associated machine-readable media provide nonvolatile storage of machine-executable instructions, data structures, program modules and other data for the computer. 
   The foregoing description of embodiments of the invention has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed, and modifications and variations are possible in light of the above teachings or may be acquired from practice of the invention. The embodiments were chosen and described in order to explain the principals of the invention and its practical application to enable one skilled in the art to utilize the invention in various embodiments and with various modifications as are suited to the particular use contemplated. 
   Those skilled in the art will appreciate that the embodiments disclosed herein may be applied to the formation of any medical navigation system. Certain features of the embodiments of the claimed subject matter have been illustrated as described herein, however, many modifications, substitutions, changes and equivalents will now occur to those skilled in the art. Additionally, while several functional blocks and relations between them have been described in detail, it is contemplated by those of skill in the art that several of the operations may be performed without the use of the others, or additional functions or relationships between functions may be established and still be in accordance with the claimed subject matter. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the embodiments of the claimed subject matter.