Abstract:
The invention is process for tracking a moving targeted vehicle from a remote sensor platform comprising the steps of 1) tracking the targeted vehicle and periodically recording its radar signature until its identity becomes ambiguous, 2) tracking the target after it has left its ambiguous state and periodically recording its radar signature; and 3) comparing the recorded radar signatures prior to the targeted vehicle becoming ambiguous to the recorded radar signature taken after the targeted vehicle has left its ambiguous state and determining that the targeted vehicle now tracked is the same as the targeted vehicle being tracked prior to becoming ambiguous.

Description:
BACKGROUND OF THE INVENTION  
       [0001]     1. Field of the Invention  
         [0002]     The invention relates to the field of sensor resources management and tracking fusion and, in particular, to the tracking of vehicles who&#39;s identity becomes ambiguous.  
         [0003]     2. Description of Related Art  
         [0004]     Tracking moving ground targets by radar from an aircraft in a battlefield situation is a difficult process. First of all, there may be a large number of moving vehicles in the vicinity of the targets of interest. In addition, the terrain and foliage can intermittently block surveillance. Thus sensor management is critical. In most previous tracking instances, the tracker was data driven. Trackers were at the mercy of the data they ingested. The only way to improve performance was to fine tune prediction models, sensor models, and association algorithms. Such fine-tuning led to improved performance, but only marginally. Potentially, trackers could realize much more significant improvements if they could manage their input data stream.  
         [0005]     Thus, it is a primary object of the invention to provide a process for improving the ability to track targets using sensor data.  
         [0006]     It is another primary object of the invention to provide a process for eliminating ambiguities when tracking vehicles.  
         [0007]     It is a further object of the invention to provide a process for eliminating ambiguities between targeted vehicles and other vehicles that come within close contact with the targeted vehicle.  
       SUMMARY OF THE INVENTION  
       [0008]     Tracking vehicles on the ground by radar from an aircraft can be difficult. First of all, there may be a multiple number of vehicles in the immediate area, with several nominated for tracking. In addition, the vehicles may cross paths with other nominated or non-nominated vehicles, or become so close to each other that their identity for tracking purposes may be come ambiguous. Thus maximizing the performance of the radar systems becomes paramount. The radar systems, which are steered array type, can typically operate in three modes:  
         [0000]     1. Moving target Indicator (MTI) mode. In this mode, the radar system can provide good kinematic tracking data.  
         [0000]     2. High range resolution (HRR) mode. In this mode, the radar system is capable of providing target profiles.  
         [0000]     3. High update rate (HUR) mode. In this mode, target is tracked at very high rate, such that the position is accurately determined.  
         [0000]     Tracking performance is enhanced if the radar is operated in the mode best suited to type of information required.  
         [0009]     An existing kinematic tracker is used to estimate the position of all the vehicles and their direction of travel and velocity. The subject process accepts the data from the kinematic tracker and maps them to fuzzy set conditions. Then, using a multitude of defined membership functions (MSFs) and fuzzy logic gates generates sensor mode control rules. It does this for every track and each sensor. The rule with the best score becomes a sensor cue.  
         [0010]     In co-pending U.S. patent application Ser. No. 10/976,150 Process for Sensor Resource Management by N. Collins, et al. filed Sep. 28, 2004, a process is disclosed for tracking at least a first targeted moving vehicle from at least one second non-targeted vehicle by means of a radar system within an aircraft, the radar having moving target indicator, high range resolution and high update rate modes of operation, the process comprising the steps:  
         [0000]     1. Tracking the kinematic quality of the vehicles by calculating position, heading, and speed uncertainty of the vehicles and providing a first set of scores therefore;  
         [0000]     2. Collecting data needed for future required disambiguations by calculating the usefulness and neediness of identification measurements of all tracked vehicles and providing a second set of scores therefore;  
         [0000]     3. Collecting required data needed for immediate disambiguation by calculating the usefulness and neediness of identification measurements of all ambiguous tracked vehicles and providing a third set of scores therefore.  
         [0000]     4. Selecting the highest over all score of from said first, second and third scores; and  
         [0011]     Cueing the radar to track the vehicle with the highest over all score to operate in the high update rate mode or, high range resolution mode, or moving target indictor mode depending upon which score is the highest score.  
         [0012]     The problem of vehicles crossing one another, or coming into close contact is what creates an ambiguity. Thus the subject invention makes use of a feature aided track stitcher (FATS). This system continuously monitors nominated vehicles and records their radar signature as a function of its angular relationship to the aircraft and stores this information in a database. Thus should two vehicles come so close together that an ambiguity is created and then separate, the FATS is used to compare the radar signature of the vehicles after separation with those in the database. If the nominated vehicle assumes an angular relationship to the vehicle that is similar to one in the database for that nominated vehicle, then the ambiguity may be removed.  
         [0013]     If there are two aircraft monitoring the area, then the second aircraft will take the second highest score with the limitation that the radar operates in a different mode to eliminate interference between the radar systems.  
         [0014]     The novel features which are believed to be characteristic of the invention, both as to its organization and method of operation, together with further objects and advantages thereof, will be better understood from the following description in connection with the accompanying drawings in which the presently preferred embodiment of the invention is illustrated by way of example. It is to be expressly understood, however, that the drawings are for purposes of illustration and description only and are not intended as a definition of the limits of the invention. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0015]      FIG. 1  is a simplified view of terrain being monitored by two aircraft.  
         [0016]      FIG. 2  is a long-term track maintenance architecture map for data fusion design.  
         [0017]      FIG. 3  is a depiction of the types of positional, direction and velocity data provided by a typical kinematic tracker.  
         [0018]      FIG. 4  is a chart of the Sensor mode options versus actions to be monitored and track status.  
         [0019]      FIGS. 5A and 5B  are a simplified process flow chart for calculation scores for radar mode selection.  
         [0020]      FIG. 6  is a flow diagram for rule number one.  
         [0021]      FIG. 7  is a flow diagram for rule numbers two.  
         [0022]      FIG. 8  is a graph illustrating a portion of the formulas for determining the score of the long time since last measurement function M 18 .  
         [0023]      FIG. 9  is a graph illustrating a portion of the formulas for determining the score of the heading uncertainty big membership function M 20   
         [0024]      FIG. 10A  is a graph illustrating the measurement of the good multi-lateration angle membership function M 31 .  
         [0025]      FIG. 10B  is a graph illustrating a portion of the formulas for determining the score of the good multi-lateration angle membership function M 31 .  
         [0026]      FIG. 11A  is a diagram of the ellipse used in the position uncertainty membership function M 19 .  
         [0027]      FIG. 11B  is a graph illustrating a portion of the formulas for determining the score of the position uncertainty function uncertainty membership function M 19 .  
         [0028]      FIG. 12  is a flow diagram for rule number three.  
         [0029]      FIG. 13  is a graph illustrating a portion of the formulas for the good track σ r  (sigma r) membership function M 37 .  
         [0030]      FIG. 14A  is a top view of a vehicle with aspect angles indicated.  
         [0031]      FIG. 14B  is a graph illustrating a portion of the formulas for the not bad side pose membership function M 32 .  
         [0032]      FIG. 15 a  graph illustrating a portion of the formulas for the availability of helpful aspect M 12 .  
         [0033]      FIG. 16  is a flow diagram for rule number four M 24 .  
         [0034]      FIG. 17  is a graph of a portion of the formula for the derivation of the closeness to nominated track membership function M 1 .  
         [0035]      FIG. 18  is a graph of a portion of the formula for the derivation of the same heading membership function M 2 .  
         [0036]      FIG. 19  is a graph of a portion of the formulas for the derivation of the similar speed membership function M 3 .  
         [0037]      FIG. 20A  is a graph of a portion of the formula for the derivation of the time-to-go (TTG) to a common intersection membership function M 4 .  
         [0038]      FIG. 20B  presents a flow diagram for determining the intersection scenario M 7 .  
         [0039]      FIG. 21  is a graph of a portion of the formulas for the derivation of the off road scenario membership function M 36 .  
         [0040]      FIG. 22  is a flow diagram for rule numbers five M 25 .  
         [0041]      FIG. 23  is a flow diagram for rule numbers six M 26 .  
         [0042]      FIG. 24  is a graph of a portion of the formulas for the derivation of the holes in the “on the fly” database M 11   
         [0043]      FIG. 25  is a graph of a portion of the formula for the derivation of the uniqueness of the available aspect membership function M 10 .  
         [0044]      FIG. 26  is a flow diagram formula number seven M 27 .  
         [0045]      FIG. 27  is a flow diagram for rule numbers eight M 28 .  
         [0046]      FIG. 28  is a flow diagram for rule numbers nine M 29 .  
         [0047]      FIG. 29  is a flow diagram for rule numbers ten M 30 .  
         [0048]      FIG. 30  is a depiction of the terrain screening scenario causing an ambiguity.  
         [0049]      FIG. 31  is a depiction of the road intersection scenario causing an ambiguity.  
         [0050]      FIG. 32  is a depiction of a first step in the elimination of an ambiguity in a road intersection scenario.  
         [0051]      FIG. 33  is a depiction of a second step in the elimination of an ambiguity in a road intersection scenario.  
         [0052]      FIG. 34  is a depiction of a third step in the elimination of an ambiguity in a road intersection scenario.  
         [0053]      FIG. 35  is a depiction of a fourth step in the elimination of an ambiguity in a road intersection scenario.  
         [0054]      FIG. 36  is a depiction of a fifth step in the elimination of an ambiguity in a road intersection scenario.  
         [0055]      FIG. 37  is first test case of the intersection scenario.  
         [0056]      FIG. 38  is second test case of the intersection scenario.  
         [0057]      FIG. 39  is third test case of the intersection scenario.  
         [0058]      FIG. 40  is fourth test case of the intersection scenario.  
         [0059]      FIG. 41  is a table summarizing the results of the test cases illustrated in  FIGS. 37, 38 ,  39 , and  40 .  
         [0060]      FIG. 42  is a disambiguate logic chart for the FATS.  
         [0061]      FIG. 43  is a Probability of feature match logic for the FATS.  
         [0062]      FIG. 44  is a top-level control chart of the feature added track stitcher FATS.  
         [0063]      FIG. 45  is a FATS system Functional architecture diagram. 
     
    
     DESCRIPTION OF THE PREFERRED EMBODIMENT  
       [0064]     Referring to  FIG. 1 , which is a simplified view of terrain wherein several vehicles are traveling and are being monitored by aircraft  10  and  12 . Because the vehicle position as well as its velocity and direction of travel are estimates, they are generally defined as “tracks”, thus vehicle and track are used interchangeably hereinafter. The terrain includes hills  16 , with several structures  18  nearby. Vehicles  20 A, and  20 B are traveling on road  21  in the direction indicated by arrow  22  toward the hills  16 . Vehicles  24 A, and  24 B are traveling on road  25 , which intersects road  21  at point  26 , while vehicle  27  and  28  are traveling toward each other on road  29  some distance away. The situation illustrated in  FIG. 1  is for purposes of illustration, for any real situation in the battlefield will be far more complex. Furthermore, while two aircraft are shown, there may be only one or more than two aircraft involved in the tracking. In addition, while aircraft are used, the system could be ground based or on ships. Thus the use of aircraft is for purposes of illustration only.  
         [0065]     The long-term track maintenance architecture map for the sensor management system design is illustrated in  FIG. 2 . There are three stages: target tracking  30 , situation assessment and priority calculation  32 , and sensor resource management (SRM)  33 . Target tracking  30  involves the use of a Kinematic Tracker program  34 , which receives input from the sensor in the moving target Indicator (MTI) mode. Kinematic tracking programs are available from such Companies as Orincon Corporation, San Diego, Calif. and Northrop Grumman Corporation, Melbourne, Fla. Tracking a vehicle that is isolated from other vehicles is easy; however, when the vehicles are in close proximity and traveling at the same or nearly the same speed, it becomes difficult (see  FIG. 1 ). Thus the second level is the use of a Feature Aided Tracking Stitcher (FATS) system  36  to refine the data provided by the kinematic tracking program  34 .  
         [0066]     The data from the FATS  36  is stored in an on-the-fly database  38 . The output from the Kinematic tracker  34  is provided to the SRM  33 , which is a process that provides the analysis of tracking information from radar systems and makes the decision as to which of three radar modes (herein after referred to as Sensor Modes) is necessary to ensure that the vehicle remains tracked. Note that the sensors by use of the Kinematic tracker  34  and FATS  36  can determine the radar cross-section (RCS), speed, direction of travel and distance to the vehicle as well as the distance between vehicles.  FIG. 3  presents a summary of the data provided by the Kinematic tracking program  34 .  
         [0067]     The three modes of the radar system are:  
         [0000]     1. Moving target Indicator (MTI) mode. In this mode, the radar system can provide good kinematic tracking data.  
         [0000]     2. High range resolution (HRR) mode. In this mode, the radar system is capable of providing target profiles.  
         [0000]     3. High update rate (HUR) mode. In this mode, the target is tracked at very high rate, such that the position is accurately determined.  
         [0068]     Tracking performance is enhanced if the radar is operated in the mode best suited to the type of information required.  FIG. 4  is chart of Sensor modes versus to be monitored and track status. The SRM uses a two-stage process to aggregate hundreds of variables and constraints into two sensor cue decisions. First, it accepts input data from the sensors and maps this to fuzzy set conditions. The system uses thirty-seven defined membership functions (MSF). A membership function is a fuzzy set concept for assigning a quantitative score to a characteristic. The score is determined by the degree to which that characteristic is met. The membership functions are as follows:  
         [0000]     MSF 1  Closeness To Nominated Track (M 1  Closeness)—How far away is a track that may be confused with the nominated track.  
         [0000]     MSF 2  Same Heading As Nominated Track (M 2  Same Heading)—If the confuser track is heading in the same direction as the nominated track.  
         [0000]     MSF 3  Similar Speed As Nominated Track (M 3  Similar Speed)—How close is the speed of a confuser track to the nominated track.  
         [0000]     MSF 4  Small Time-To-Go To Common Intersection Of Nominated Track (M 4  Small TTG To Common Intersection)—Is the time that a confuser track is from an intersection to which a nominated track is heading small.  
         [0000]     MSF 5  Similar Time-To-Go to Common Intersection As Nominated Track (M 5  Similar TTG to Common Intersection)—Confuser Track has about the same time-to-go to the same intersection as the nominated track is heading towards.  
         [0000]     MSF 6  Passing Scenario Confuser Track to Nominated Track (M 6  Passing Scenario)—It is the minimum of MSF  1 ,  2 , and  3 .  
         [0000]     MSF 7  Common Intersection Factor To Nominated Track (M 7  Intersection Scenario)—It is the maximum of MSF  4  and  5 .  
         [0000]     MSF 8  Confuser Factor (M 8  Confuser Status)—It is the maximum of MSF  6  and  7 .  
         [0000]     MSF 9  Nominated Status (M 9  Nomination Status)—The track is nominated by the operator or is ambiguous with a nominated track.  
         [0000]     MSF 10  Uniqueness Of Available Aspect of Vehicle to fill hole (M 10  Unique Aspect)—Has the FATS system provided a new aspect of the vehicle at an angle not already in the data base.  
         [0000]     MSF 11  Holes In “On-The-Fly” database (M 11  Holes In db)—Does this track have big gaps of missing aspect angle coverage in the “on-the-fly” database.  
         [0000]     MSF 12  Helpfulness of Aspect Angle to Disambiguate (M 12  Helpful Aspect)—Will a profile at this predicted aspect angle help to disambiguate the track. Are there similar aspects already in the “on-the-fly” database.  
         [0000]     MSF 13  Poor Kinematic Quality (M 13  Poor Kin. Qual.)—Minimum of MSF  18 ,  19 , and  20   
         [0000]     MSF 14  Track Not Screened (M 14 )—Is track screened by terrain or trees, etc.  
         [0000]     MSF 15  Track is not kinematically ambiguous (M 15  Not Kin Ambig.)—Is track not close to other tracks.  
         [0000]     MSF 16  Track Not in Limbo (M 16  Not Limbo)—Is track identified by FATS as ambiguous (0 or 1).  
         [0000]     MSF 17  Track in Limbo (M 17  Limbo or ambiguous) Track marked by FATS as ambiguous (0 or 1).  
         [0000]     MSF 18  Time Since Last Measurement (M 18  Long Time Since Last Measurement) How long has track gone without an updating measurement.  
         [0000]     MSF 19  Position Uncertainty Big (M 19  Position Uncertainty)—Does the track&#39;s covariance matrix reflect that the track&#39;s position estimate is of a low quality.  
         [0000]     MSF  20  Heading Uncertainty Big (M 20  Heading Uncertainty)—Does the track&#39;s covariance matrix reflect that the track&#39;s heading estimate is of a low quality.  
         [0000]     MSF 21  is the score for Rule.  
         [0000]     MSF 22  is the score for Rule  2 .  
         [0000]     MSF 23  is the score for Rule  3 .  
         [0000]     MSF 24  is the score for Rule  4 .  
         [0000]     MSF 25  is the score for Rule  5 .  
         [0000]     MSF 26  is the score for Rule  6 .  
         [0000]     MSF 27  is the score for Rule  7 .  
         [0000]     MSF 28  is the score for Rule  8 .  
         [0000]     MSF 29  is the score for Rule  9 .  
         [0000]     MSF 30  is the score for Rule  10 .  
         [0069]     MSF 31  Helpful Multi-Lateral Angle (M 31  Good Multi-Lat Angle)—Is the current line-of-sight angle from the sensor to the track a good angle to reduce position error covariance? MSF 32  Not A Bad Pose or Low Minimum Detectable Velocity (M 32  Not Bad Pose)—Is the current aspect angle to the track not a good pose for use of Profiles (Side poses do not work as well)  
         [0000]     MSF 33  In Field Of View (M 33 FOV)—Is track in field of view of sensor.  
         [0000]     MSF 34  Small Distance Of Closest Separation (M 34  Small Distance Of Closest Separation)—Will this confuser track be close to the nominated track at the predicted closest point of spearation.  
         [0000]     MSF 35  Small Time-To-Go To Closest Separation (M 35  Small TTG to closest separation)—Is the time-to-go until the predicted point of closest separation small? 
         [0000]     MSF 36  Off-Road Scenario (M 36  Off-Road Scenario)—Minimum of M 34  and M 35 .  
         [0000]     MSF 37  Good Sigma Range Rate Estimate (M 37  Good σ r )—Does the current track&#39;s covariance matrix reflect that the track has a good quality range rate estimate?.  
         [0000]     MSF 38  Clearness Of Track M 38  Clearness)—How far away is track from other tracks.  
         [0070]     Fuzzy logic gates are used to generate ten sensor mode control rules, shown in  FIGS. 5A and 5B , which are multiplied by weighing factors (to be subsequently discussed) to determine a rule score. It does this for every track and each sensor. The rule with the best score becomes a sensor cue. The second sensor cue, if there is a second sensor, is the next best score. In this way, the fuzzy network decides which of three modes to request and where to point each sensor. Again note that while two sensors will be discussed, a single sensor or more than two could be used.  
         [0071]     Referring to  FIGS. 3-5  and additionally to  FIG. 6 , rule number 1 (indicated by numeral  40 ), is illustrated in detail. Rule number 1 is the only hard or non-fuzzy logic rule. Rule  1  provides a HUR Burst data when a key track (nominated) is near an intersection or near other vehicles. This is to maintain track continuity when a nominated track is predicted to be taking a maneuver or predicted to be close to other tracks (confusers).  
         [0072]     1. It is first necessary to determine the nomination status of the track (M 9 ). Nomination status is assigned a one if it is a nominated track OR track is ambiguous with a nominated track (determined by either the kinematic tracker  34  or “FATS” system  36  to be subsequently discussed) AND is kinematically ambiguous (M 1 ) AND  
         [0000]     2. Track is in field of view (M 32 ) AND not terrain screened (M 14 ) AND not near an intersection (M 4 ).  
         [0000]     The resulting score is multiplied by weighing factor W 1  to provide the rule number one score.  
         [0073]     The track is nominated (M 9 ) by the operator as one of importance and Kinematically ambiguous (M 17 ) status is determined by the kinematic tracker  34  or FATS  36 , to be subsequently discussed. The calculation of the clearness M 38  score is as follows:  
         [0000]     Given:  
         [0000]     From the kinematic Tracker  
         [0000]     Xt1=Track one X position in meters (eastward)  
         [0000]     Yt1=Track one Y position in meters (northward)  
         [0000]     Yt1=Track two X position in meters (eastward)  
         [0000]     Xt2=Track two Y position in meters (northward)  
         [0000]     From the aircrafts navigation system  
         [0000]     XP=Sensor aircraft X position in meters (eastward)  
         [0000]     YP=Sensor aircraft Y position in meters (northward)  
         [0000]     Then:  
         [0000]     D=Distance Track 1 to Track 2 
 
 D=√{square root over (((Xt1−Xt2)     2     +(Yt1−Yt2)     2     ))} 
 
 R1=Range of sensor aircraft to Track 1 
 
 R 1=√{square root over ((( XP−Xt 1) 2 +( YP−Yt 1) 2 ))}
 
 R2=Range of sensor aircraft to track 2 
 
 R 2=√{square root over ((( XP−Xt 2) 2 +( YP−Yt 2) 2 ))}
 
 ΔR=Approximate down range difference between tracks 
 
Δ R=R 1 −R 2 
 
 ΔXR=Approximate cross range difference between tracks 
 
 ΔXR= √{square root over ( D   2 −Δ R   2 )}
 
 If ΔR&gt;D 1  or ΔXR&gt;D 2 , Score=1 
 
 If not,  
       Score   =         (       Δ   ⁢           ⁢   R     -     D   1       )     *     (       Δ   ⁢           ⁢   XR     -     D   2       )           D   1     *     D   2             
 
 Where D 1, =Minimum distance threshold, D 2 =maximum distance threshold. The clearness score typically must be greater than 0.2 for the sensors presently being used; they may vary from sensor to sensor. 
 
         [0074]     As to the Time-To-go to the nearest intersection (M 4 ), the vehicle speed and position are known, as well as the distance to the nearest intersection. Thus the time can be easily computed. For the sensors presently used, the time must be less than five seconds  
         [0075]     Still referring to  FIGS. 3-5  and additionally to  FIG. 7 , the second rule, designated by numeral  42 . provides standard MTI data for nominated track who currently have marginal kinematic estimation accuracy for estimation improvement and includes the following steps:  
         [0076]     1. If nominated track (M 9 ) OR track ambiguous with nominated track (M  9 ) AND poor kinematic quality (M 13 ), which comprises time since last measurement is long (M 18 ), or position uncertainty (M 19 ), AND helpful multi-lateral angle (M 31 ) OR heading uncertain (M 20 ). AND  
         [0000]     2. Track is not terrain screened AND track is in FOV (M 33 ) AND not in discrete area (M  38 ).  
         [0000]     The result of Rule  2  is multiplied by weighting factor W 2 , which provides Rule  2  score (M 22 ).  
         [0077]     The Membership function M 13  poor kinematic quality is a function of M 19  position uncertainty, M  31  Good Multi-lateral angle, M 18  Time since last measurement and M 20  heading uncertainty.  
         [0078]     Following are the calculations for M 18  Long time since last measurement M 18 .  
         [0000]     Given ΔT=average sensor revisit rate. 
 
T 1 ≅2ΔT 
 
T 2 ≅10ΔT 
 
 TLM=Time since last measurement. 
 
If TLM≦T 1  
 
Then Score=0 
 
If TLM≧T 2  
 
Then Score=1 
 
If T 1 &lt;TLM&lt;T 2  
 
 Then:  
       Score   =       (       TLM   -     T   1           T   2     -     T   1         )     2         
 
 See  FIG. 8  for graph  44 . 
 
         [0079]     The heading uncertainty function (M 20 ) is calculated using the following formula. The heading σ h  is first calculated using location values from the kinematic tracker.  
         σ   H     ≅       (         Y   *     P   XX       -     2   *   X   *   Y   *     P   XX       +     X   *     P   YY             X   2     +     Y   2         )     *     180   π           
 
 Then if: 
 
σ H &lt;A 1 , 
 
Then Score=0 
 
σ H &gt;A 2 , 
 
Then Score=1 
 
A 1 &lt;σ H &gt;A 2 , 
 
 Then:  
         Score   =         σ   H     -     A   1           A   2     -     A   1           ,       
 
 See graph  48  in  FIG. 9 . 
 
 The value of A 1  and A 2  of course will depend upon the sensor system. Note that uncertainty varies as the square of the time elapsed from the last measurement because of the possibility of acceleration of he vehicle. 
 
         [0080]     The formula for determining the good multi-lateral angle M 31  is provided in  FIG. 10B  and is discussed below. The first calculations require the determination of the angular relationships between the aircraft  10  and track  50  indicated ( FIG. 10A ). The Xt, Xp, Yt, Pxy, Pyy and Pxx values are all obtained from the kinematic tracker system  34  (FIG.  
       AZ   =       Tan     -   1       ⁡     (         X   T     -     X   P           Y   T     -     Y   P         )           
         θ   O     =         Tan     -   1       ⁡     (       2   ⁢     P   XY           P   YY     -     P   XX         )       2         
       DA   =          AZ   -     θ   O                
       Score   =         DA   *     (       A   1     -   1.0     )       90     +   1.0         
 
 See graph  52  in  FIG. 10B . 
 
 Where A 1 =An initial setting depending upon system sensors. 
 
         [0081]     The Position uncertainty (M 19 ) is determined by calculating the area of an ellipse  56 , as illustrated in  FIG. 11A , in which it is estimated that the vehicle being tracked resides.  
         Major   ⁢             ⁢             ⁢   axis     =         P   XY     +       P   XX     ⁢       +         P   YY   2     +     P   XX   2     +     4   ⁢     P   XY   2       -     2   ⁢     P   xx     *     P   YY             2               
         Minor   ⁢             ⁢             ⁢   axis     =         P   YY     +       P   XX     ⁢       -         P   YY   2     +     P   XX   2     +     4   ⁢     P   XY   2       -     2   ⁢     P   XX     *     P   YY             2               
 
 Pxx, Pyy, Pxx 2 , Pxy 2 , Pyy 2  are measurements provided by the kinematic tracker system  34  ( FIG. 2 ). 
 
Area=π*major axis*minor axis 
 
If area&lt;A 1 , 
 
Then score=0 
 
If area&gt;A 2 , 
 
Then score=1 
 
 In between A 1  and A 2 , then:  
         Score   =       Area   -     A   1           A   2     -     A   1           ,       
 
 see graph  53  in  FIG. 11B  
 
 The value of A 1  and A 2  are determined by the capability of the sensor system. 
 
         [0082]     Still referring to  FIGS. 4-6 , and additionally to  FIG. 12 , rule  3 , and indicated by numeral  59 , requests HRR on nominated tracks to get profiles to try to disambiguate tracks that are now in the open but in the past where ambiguous with other tracks. Rule  3  is follows: 
        1. If nominated track (M 9 ) OR track ambiguous with nominated track is in limbo (M 17 ) AND 
 
 2. A helpful aspect is available (M 12 ) and not bad side pose (M 32 ) and not in discrete area (M 38 ) AND 
 
 3. Track is not kinematically ambiguous (M 15 ) AND 
 
 4. Track has good sigma range rate (M 37 ). 
 
 The result is multiplied by Weighting factor W 3  to provide Rule  3  score (M 23 ). 
       
 
         [0084]     Referring to  FIG. 13 , The good track σ R′ , standard deviation of range rate (M 37 ) is easily determined by the kinematic tracker program  34 .  
         [0000]     First calculate relative north and east from aircraft:  
         [0000]     Y T =North Position of Track.  
         [0000]     Y P =North Position of Aircraft.  
         [0000]     T=East Position of Track.  
         [0000]     X P =East Position of Track. 
 
 N=Y   T   −Y   P  
 
 E=X   T   −X   P  
 
 N′=Y   T   ′−Y   P ′
 
 E′=X   T   ′−X   P ′
 
 Then calculate horizontal range R: 
 
 R= √{square root over ( N   2 − E   2 )}
 
 Calculate H transformation:  
       H11   =       E   *     (         N   ′     *   E     -     N   *     E   ′         )         R   3           
       H12   =     N   R         
       H14   =       N   *     (       N   *     E   ′       -       N   ′     *   E       )         R   3           
       H15   =     E   R         
 
 Where D=Target speed. 
 
 Thus:  
         σ   R   ′     =                 H11   2     *     P   YY       +     2   *   H11   *   H12   *     P     YY   ′         +     2   *   H11   *   H14   *     P   YX       +                 2   *   H11   *   H15   *     P     YX   ′         +     H12   *     P     YY   ′         +     2   *   H12   *   H14   *     P       Y   ′     ⁢   X         +                 2   *   H12   *   H15   *     P       Y   ′     ⁢     X   ′           +       H14   2     *     P   XX       +     2   *   H14   *   H15   *     P     XX   ′         +                 H15   2     *     P       X   ′     ⁢     X   ′                       
 
 Knowing σ R′ 
 
If σ R′ &lt;V 1 , 
 
Then Score=1 
 
If σ R′ &gt;V 2 , 
 
then Score=0 
 
If V 1 &lt;σ R′ &lt;V 2 , 
 
 Then:  
       Score   =           V   1     -     σ     r   ′             V   2     -     V   1         +   1         
 
 and as illustrated in the graph  60  in  FIG. 13 . 
 
 Where the value of V 1  is the minimum valve and V 2  is the maximum value in meters per second. 
 
         [0085]     Referring to  FIGS. 14A and 14B , it can be seen that the not bad side pose (M 32 ) is also easily calculated and depends on the viewing angle of he vehicle shown in  FIG. 14A . 
 
If 80°&lt;Aspect angle&lt;100°, 
 
Score=0 
 
If Aspect angle&lt;80°, 
 
 Then:  
       Score   =     1   -       Aspect   .   Angle     80           
 
If Aspect angle&gt;100°, 
 
 Then:  
       Score   =         Aspect   .   Angle     -     100   •       80         
 
 A graph  62  in  FIG. 14B  plots these relationships. 
 
         [0086]     The availability of a helpful Aspect function (M 12 ) is also easily determined using the following equations:  
         [0000]     Given |Δ Heading|=Absolute value of difference in heading between two vehicles. 
 
If |Δ Heading|&gt;A 2 , 
 
Then Score=0 
 
If A 1 &lt;|Δ Heading|&lt;A 2 , 
 
 Then:  
         Score   =           A   1     -          Δ   ⁢           ⁢   Heading                A   2     -     A   1         +   1       ,       
 
 As illustrated in the graph  62  in  FIG. 15 . 
 
If |Δ Heading|&lt;A 1  
 
Then score=1 
 
 In order to disambiguate using profile matching, the profiles matched must be at nearly the same aspect angle. The helpful aspect membership functions quantifies the fuzzy membership (0.0 to 1.0) of the “helpfulness” of a collected new profile based upon how far away it is from the existing profiles in the Track&#39;s ‘on-the-fly’ profile database. If the collection aspect angle is close to the closest stored profile, it will be completely helpful, (Score=1.0). If the aspect angle is different, say over over 15 degrees away from the nearest stored profile, it will be completely useless (score=zero). In between, the usefulness will vary. 
 
         [0087]     Referring to  FIG. 16 , rule  4 , indicated by numeral  64 , provides standard MTI data for tracks deemed to be potential confuser tracks with a nominated track, which currently have marginal kinematic estimation accuracy for estimation improvement. Rule  4  is as follows:  
         [0000]     1. If a track is a confuser status (M 8 ) to a (nominated track OR track is ambiguous with nominated track) AND  
         [0000]     2. Has poor kinematic quality (M 13 ) AND track is not terrain screened (M 38 ) AND track is in field of view (M 33 ) AND not in discrete area,  
         [0000]     The resulting score multiplied by W 4  provides the rule  4  score M 24 .  
         [0088]     M 8  confuser status is determined by:  
         [0000]     1. If a track (is close to nominated track (M 1 ) AND at the same heading (M 2 ) AND at a similar Speed (M 3 )), which is the passing scenario (M 6 ) OR  
         [0000]     2. Has a small time-to-go to a common intersection with a nominated track (M 4 ) AND a similar time-to-go to a common intersection (M 5 ) OR  
         [0000]     3. Has a small-predicted distance of closest separation to a nominated track M 34 ) AND a small Time-to-Go to predicted time of closest separation M 35 .  
         [0089]     The closeness to nominated track membership function M 1  is also easily determined. 
 
If no nominated track, 
 
Then Score=0 
 
If D&gt;D 2 , 
 
Then Score=0 
 
If D&lt;D 1 , 
 
Then Score=1 
 
If D 1 &lt;D&lt;D 2 , 
 
 Then:  
       Score   =           D   1     -   D         D   2     -     D   1         +   1         
 
 See graph  65  in  FIG. 17 . 
 
 Where: 
 
 D 1  is the minimum distance threshold and D 2  is the maximum distance threashold. 
 
         [0090]     The formulas for calculating the same heading membership M 2  are as follows. 
 
If no nominated track, 
 
Then Score=0 
 
If |A Heading|&gt;A 2 , 
 
Then Score=0 
 
If |Δ Heading|&lt;A 1 , 
 
Then Score=1 
 
If A 1 &lt;|Δ Heading|&lt;A 2 , 
 
 Then:  
         Score   =           A   1     -          Δ   .   Heading                A   2     -     A   1         +   1       ,       
 
 See graph  65  in  FIG. 18  
 
 Where A 1  and A 2  are minimum and maximum angles. 
 
         [0091]     The formulas for calculating the similar speed membership function M 3  are as follows: 
 
If no nominated track, 
 
Then Score=0 
 
If ΔSpeed&gt;V 2 , 
 
Then Score=0 
 
If ΔSpeed&lt;V 1 , 
 
Then Score=1 
 
If V 1 &lt;ΔSpeed&lt;V 2 , 
 
 Then:  
               V   1     -     Δ   .   Speed           V   2     -     V   1         +   1     ,       
 
 See graph  69  in  FIG. 19  
 
 Where V 1  and V 2  are minimum and maximum thresholds in speed. 
 
         [0092]     The formulas for the calculation of Off road scenario-Closest Separation M 34  are as follows:  
         [0000]     Following is calculation for closest separation distance of the nominated track and a track of interest and the calculation of the Time-To-Go (TTG) to closest separation.  
         [0000]     Obtain required terms from track  
         [0000]     X i =Track of interest X (East) Position  
         [0000]     Y i =Track of interest Y (North) Position  
         [0000]     VX i =Track of interest X (East) Speed  
         [0000]     VY i =Track of interest Y (East) Speed  
         [0000]     X n =Track of interest X (East) Position  
         [0000]     Y n =Track of interest Y (North) Position  
         [0000]     VX n =Track of interest X (East) Speed  
         [0000]     Vy n =Track of interest Y (North) Speed  
         [0093]     Calculate intermediate variables  
       a   =       X   n     -     X   i           
       b   =       VX   n     -     VX   i           
       c   =       VY   n     -     VY   i           
       d   =       VY   n     -     VY   i           
       TTG   =         a   *   b     +     c   *   d           b   2     +     d   2             
 
 If TTG is positive the vehicles are approaching other, calculations proceed. Calculate closest separation distance D. 
 
 D=√{square root over ((a+b*TTG) 2 +(c+d*TTG) 2 )}
 
 With this information, the TTG Small function (M 4 ) and TTG Similar (M 5 ) function and M 7  function can be determined. 
 
If no nominated track, 
 
Then Score=0 
 
If no common intersection, 
 
Then Score=0 
 
If TTG&gt;T 2 , 
 
Then Score=0 
 
If T 1 &lt;TTG&lt;T 2 , 
 
 Then:  
         Score   =           T   1     -   TTG         T   2     -     T   1         +   1       ,       
 
 See graph  70 ,  FIG. 20A  
 
If TTG&lt;T 1 , 
 
Then Score=1 
 
 The T 1  and T 2 , values are minimum and maximums. 
 
 Note that given the above, a determination whether the track is considered a confuser track (M 7 ) can be determined (See  FIG. 20B ) 
 
         [0094]      FIG. 22  illustrates rule  5  (M 25 ), indicated by numeral  72 , requests HRR on confuser tracks to get profiles to try to disambiguate tracks that are now in the open but in the past where ambiguous with other tracks. Rule  5  is as follows:  
         [0000]     1. If a track is a confuser to a (nominated track OR track ambiguous with nominated track (M 8 ) AND is in limbo (M 17 ) AND  
         [0000]     2. A helpful aspect (M 12 ) is available AND not side pose (M 32 ) AND  
         [0000]     3. Track is not terrain screened (M 14 ) AND track is in field of view (M 33 ) AND not in discrete area (M 38 ) AND  
         [0000]     4. Track is not kinematically ambiguous AND  
         [0000]     5. Track has good sigma range rate.  
         [0000]     The score multiplied by W 5  provides the rule  5  score M 25 .  
         [0095]      FIG. 23  illustrates rule  6 , and indicated by numeral  74 , HRR on unambiguous nominated tracks to get profiles to fill-up the on-the-fly data base for fingerprinting of the important track for possible disambiguation, if required, at a later time. Rule  6  is as follows:  
         [0000]     1. If nominated track (M 9 ) OR track ambiguous with nominated track is not in limbo (M 16 ) AND  
         [0000]     2. Track has holes in “on the fly” data base (M 11 ),  
         [0000]     3. A unique/helpful aspect is available (M 10 ) AND track not bad pose (M 32 ) AND  
         [0000]     4. Track is not terrain screened (M 14 ) AND track is in field of view ((M 33 ) AND not in discrete area (M 38 ) AND  
         [0000]     5. Track is not kinematically ambiguous (M 15 ) AND  
         [0000]     6. Track has good sigma range rate.  
         [0000]     The score multiplied by W 6  provides the rule  6  score M 26   
         [0096]     Following is the calculation of Holes in on the fly database (M 11 ):  
         Score   =     1   -       Number.of.profiles.in.regular.database.         360   /   Δ     ⁢           ⁢   θ           ,       
 
 See graph  75 ,  FIG. 24 , 
 
 Where Δθ=Resolution of database. 
 
         [0097]     Following is the calculation of the uniqueness of available aspect M 10 . 
 
If Dθ&lt;A 1 , 
 
Score=0 
 
If Dθ&gt;A 2 , 
 
Score=1 
 
If A 1 &lt;Dθ&lt;A 2 . 
 
 Then:  
         Score   =         D   ⁢           ⁢   θ     -     A   1           A   2     -     A   1           ,       
 
 See graph  76 ,  FIG. 25  
 
 Where A 1  is minimum angle threshold and A 2  is maximum angle threshold. 
 
         [0098]      FIG. 26  presents rule  7 , indicated by numeral  78 , requests HRR on unambiguous nominated tracks to get profiles to fill-up the on-the-fly data base for fingerprinting of the important track for possible disambiguation, if required, at a later time. Rule  7  is as follows:  
         [0000]     1. If track is a confuser to (a nominated track OR track ambiguous with nominated track (M 8 ) and is not in limbo. (M 16 ) AND  
         [0000]     2. Track has holes in “on the fly” data base (M 11 ),  
         [0000]     3. A unique/helpful aspect is available (M 10 ) AND track not bad pose (M 32 ) AND  
         [0000]     4. Track is not terrain screened (M 14 ) AND track is in field of view ((M 33 ) AND not in discrete area (M 38 ) AND  
         [0000]     5. Track is not kinematically ambiguous (M 15 ) AND  
         [0000]     6. Track has good sigma range rate (M 37 ).  
         [0000]     The score multiplied by W 7  provides the rule  7  score M 27 .  
         [0099]      FIG. 27  presents rule  8 , indicated by numeral  80 , standard MTI data for background surveillance track who currently have marginal kinematic estimation accuracy for estimation improvement. Rule  8  is as folllows:  
         [0000]     1. If track has poor kinematic quality (M 13 ) and not nominated (M 9 ) AND not terrain screened (M 14 ) AND not in discrete area (M 38 ) AND in field of view (M 33 ).  
         [0000]     The score multiplied by W 8  provides the rule  8  score M 28 .  
         [0100]      FIG. 28  presents rule  9  (M 29 ), indicated by numeral  82 , requests HRR on confuser tracks to get profiles to try to disambiguate background surveillance tracks that are now in the open but in the past where ambiguous with other tracks. Rule  9  is as follows:  
         [0000]     1. If regular surveillance track is (not nominated or a confuser) in limbo, AND  
         [0000]     2. A unique/helpful aspect is available (M 12 ) and Not bad pose (M 32 ), AND  
         [0000]     3. Track is not terrain screened (M 14 ) and track is in field of view (M 33 ) and not in discrete area (M 38 ), AND  
         [0000]     4. Track is not kinematically ambiguous (M 15 ), AND  
         [0000]     5. Track has good sigma range rate (M 37 )  
         [0000]     The score multiplied by W 9  provides the rule  0  score M 29 .  
         [0101]      FIG. 29  presents rule  10 , indicated by numeral  84 , requests HRR on unambiguous background surveillance tracks to get profiles to populate the on-the-fly data base for fingerprinting of the track for possible disambiguation at a later time. Rule  10  is as follows:  
         [0000]     1. If a regular surveillance track (not nominated or a confuser) not in limbo (M 16 )  
         [0000]     2. Has holes in “on the fly” data base (M 11 ), AND  
         [0000]     3. A unique/helpful aspect is available (M 10 ) and not bad pose (M 32 ), AND  
         [0000]     4. Track is not terrain screened (M 14 ) and track in field of view (M 33 ) and not in discrete area (M 38 ), AND  
         [0000]     5. Track is not kinematically ambiguous (M 15 ), AND  
         [0000]     6. Track has good sigma range rate (M 37 ).  
         [0000]     The score multiplied by W 10  provides the rule  10  score M 30 .  
         [0102]     The weights W 2  to W 10  proved the system the ability to “tune” the process to place more or less emphasis on each individual rule&#39;s degree of influence, or weight, on the overall radar mode selection.  
         [0103]     Thus it can be seen that rules  1 ,  2 ,  4  and  8  are attempts to improve kinematic quality by calculating position, heading, and speed uncertainty of the tracked vehicles and providing a first set of scores therefore. Rules  6 ,  7  and  10  attempt to collect required data needed for future required disambiguations by calculating the usefulness and neediness of identification measurements of all tracked vehicles and providing a second set of scores therefore. Rules  3 ,  5  and  9  are attempts to collect required data needed for immediate disambiguation by calculating the usefulness and neediness of identification measurements of all ambiguous tracked vehicles and providing a third set of scores therefore. The highest score of all the rules determines which mode the radar operates in. With the above process, the effectiveness of the radar system is is greatly improved over traditional techniques.  
         [0104]     The subject of this invention is the FATS program, which helps resolve kinematically ambiguous tracks. Referring to  FIG. 30 , the first typical problem occurs when two vehicles  85  and  85 B approach and disappear behind foliage or terrain such as a mountain  86 , and then reemerges into view. The question is have the the two vehicles swapped their extrapolated tracks. Referring to  FIG. 31 , a more common problem is when the two vehicles,  85 A and  85 B, approach an intersection  87 . At the intersection  87 , the two vehicles are so close that it is impossible to distinguish between the two. If both vehicles turn, the problem again becomes identifying which vehicle is which. The FATS program reduces the possibility of the two vehicles remaining ambiguous by learning the radar signatures of the two vehicles at various angles to the aircraft prior to the point where they are so close that they become ambiguous.  
         [0105]     Thus referring to  FIG. 32 , when the two vehicles approach each other, the radar profiles or signatures are obtained and stored in the “on the fly” data base; in this case at time equals t 3 . Thus vehicle  1  is at 210 degrees and vehicle  2  is at 30 degrees. Referring to  FIG. 33 , at t 5  the vehicles have become ambiguous. In  FIG. 35 , the vehicles have now separated, but the track segments are ambiguous. However, at t 7  radar profiles are again recorded. Referring to  FIG. 36 , the vehicles have now turned again and at t 11  profile matches can be made with profiles collected at t 7  as shown in  FIG. 35  and the vehicles identified. The profile matching is accomplished by the root mean square test, however other techniques can be used.  FIG. 36 , the FATS continues to record radar profiles.  
         [0106]     Referring to  FIG. 37 , is an actual test scenario (case 1) wherein 2 vehicles  89 A and  89 B approach each other on tracks  90 A and  90 B, respectfully. The FATS builds a database on both vehicles  89 A and  89 B as they approach the ambiguous area  92 . Both vehicles  89 A and  89 B enter the ambiguous area  92  and travel on segments  93 A and  93 B and then turn on to segments  94 A and  94 B. While on segments  93 A and  93 B they are in limbo, because no profile exits for the vehicles in this position. However, a match is made when vehicle  98 A travels over segment  94 A. The match verified that there is no kinematic miss-association back at the intersection, no track tag change (the FATS system miss-identifies the vehicle tracks), there is a positive match, and all “on the fly” databases are converted to unambiguous.  
         [0107]     Referring to  FIG. 38 , is second actual test scenario (case 2) wherein 2 vehicles  96 A and  86 B approach each other on tracks  97 A and  97 B, respectfully. The FATS system builds a database on both vehicles  96 A and  96 B as they approach the ambiguous area  98 . Both vehicles  96 A and  96 B enter the ambiguous area  98  and travel on segments  99 A and  99 B and then turn on to segments  100 A and  100 B. While on segments  100 A and  100 B they are in limbo, because no profile exits for the vehicles in this position. When vehicle  96 A turns on segment  102 A a no match is made because vehicle  96 A is moving toward the sensor. However, vehicle  96 B turns on to segment  102 B, an attempted comparison of vehicle&#39;s  96 B profile will fail. This of course will indicate that vehicle  96 A is on segment  102 A. Here there is no kinematic miss-association back at the intersection, no track tag change is needed (the FATS system did not mis-identify the vehicle tracks), there is a positive match, and all “on the fly” data bases are converted to unambiguous  
         [0108]      FIG. 39  is third actual test scenario (case 3) wherein 2 vehicles  106 A and  106 B approach each other on segments  107 A and  107 B, respectfully. The FATS system builds a database on both vehicles  106 A and  106 B as they approach the ambiguous area  108 . Thereafter vehicle  106 A turns on to segment  110 A and then on to segment  112 A. However, the FATS system has assumed that vehicle  106 A has turned on to segment  110 B and then on to segment  112 B indicated by track  113 . On the other hand, vehicle  106 B travels down segment  110 B and onto segment  112 B. However, the FATS system has assumed that vehicle  106 B is on track  114 . When the FATS system compares the profile of vehicle  106 B on segment  112 B to the profile taken of vehicle  106 A on segment  107 A, it will determine that the tracks of vehicle  106 A and  106 B must be exchanged. Here there is a kinematic miss-association back at the intersection, and a track tag change is required (the FATS system miss-identifies the vehicle tracks), there is a negative profile match, and all “on the fly” data bases are converted.  
         [0109]      FIG. 40 , is fourth actual test scenario (case 4) wherein 2 vehicles  116 A and  116 B approach each other on segments  117 A and  117 B, respectfully. The FATS system builds a database on both vehicles  116 A and  116 B as they approach the ambiguous area  118 . Thereafter vehicle  116 A turns on to segment  120 A and then on to segment  122 A. However, the FATS system has assumed that vehicle  116 A has turned on to segment  120 B and then on to segment  112 B indicated by track  123 . On the other hand, vehicle  116 B travels down segment  120 B and onto segment  122 B. However, the FATS system has assumed that vehicle  116 B is on track  124 . When the FATS system compares the profile of vehicle  116 B on segment  122 A to the profile taken of vehicle  110 A on segment  117 A, it will determine that the tracks of vehicle  116 A and  116 B must exchanged. Here there was a kinematic miss-association back at the intersection, and therefor a track tag change is required (the FATS system miss-identifies the vehicle tracks), there is a positive profile match, and all “on-the-fly” databases are converted.  
         [0110]      FIG. 41  presents a chart summarizing the results of the four cases. The  FIGS. 42, 43  and  44  present a summary of the FATS system logic. The Functional Architecture for FATS is shown in  FIG. 45 . Descriptions of the individually numbered elements are as follows:  
         [0000]     Step  130  New track “On the fly” Data Initiation—Updates the tracks in the FATS database with new measurements received.  
         [0000]     Step  132  Fats Measurement Up Dates-Declares tracks&#39; ambiguous after coasting (with no updates) for a specified time period.  
         [0000]     Step  134  Timeout Ambiguous—Modifies the database for a track that has been dropped (removes associations with other tracks within the database).  
         [0000]     From Step  132 :  
         [0000]     Step  136  Determines when tracks become ambiguous with each other in confusing kinematic situations  
         [0000]     Step  138  Ambiguous Correlation Test-Updates the “on-the-fly” database with a measurement profile if the track is unambiguously correlated to the measurement it is paired with.  
         [0111]     Step  140  Update “On the fly” Data Structures—The process of disambiguating tracks that have interacted with other confuser tracks. The process determines whether the current profile collected within the current measurement shows that it came from the same vehicle or a different one. Action is taken if a same or different declaration is found.  
         [0000]     Step  142  Disambiguates—Initializes FATS data for a newly established track.  
         [0000]     Step  144  Find Ambiguous Combinations—Finds potential combinations of ambiguous tracks for possible association.  
         [0000]     From Step  142 :  
         [0000]     Step  146  Probability Of Feature Match—Determines the probability of a match using feature matching.  
         [0000]     From Step  156 :  
         [0000]     Step  148  Retrieve Closest Feature—Retrieves the closest HRR profiles within the database that matches the aspect of the track  
         [0000]     Step  150  Compute Feature Match Score-Probability Of Feature Match-Computes the mean square error score for the profile extracted from the tracks&#39; database and the profile within the current measurement  
         [0000]     Step  152  Range Extent Estimation Evidence Accumulation—Estimates the range extent of the signature within the HRR profile. This is used to validate whether the HRR profile matches an estimate of the targets length based on the tracks&#39; pose.  
         [0000]     From Step  142   
         [0000]     Step  154  Evidence Accumulation—Accumulates same/difference evidence for each track pairing combinations as HRR profile features are collected for that track.  
         [0112]     Step  156  Perform the Dempster—Shaeffer combine. This function uses the correlation probabilities returned when comparing profiles and updates the combined evidence state. The combined evidence state is then used to determine whether the vehicle is the same or different during the disambiguation process.  
         [0000]     Step  158  Dempster Retract—The Dempster retract un-does a previous combine if necessary.  
         [0000]     Step  160  Track Stitcher—“Stitches”, or pieces together tracks and determines whether an “id” swap is necessary between two tracks.  
         [0000]     Step  162  Combine “On the fly” Databases—Combines the profiles collected in the tracks&#39; “limbo” database with the tracks&#39; “on-the-fly” database.  
         [0000]     From Step  142   
         [0000]     Step  164  Ambiguity Resolution—Resolves ambiguities between tracks using the process of elimination. Operates on the track&#39;s ambiguity matrix.  
         [0113]     Thus it can be seen that the FATS system, by means of storing vehicle profiles in a “on the fly” data base can be used to can greatly reduce ambiguities in tracking vehicles and the like, when such vehicles come in close contact with others.  
         [0114]     While invention has been described with reference to a particular embodiment, it should be understood that the embodiment is merely illustrative, as there are numerous variations and modifications, which may be made by those skilled in the art. Thus, the invention is to be construed as being limited only by the spirit and scope of the appended claims.  
       INDUSTRIAL APPLICABILITY  
       [0115]     The invention has applicability to electronic war equipment industry.