Method and apparatus for accurate aircraft and vehicle tracking

• US 20040189521A1
• Filed: 12/23/2003
• Published: 09/30/2004
• Est. Priority Date: 03/05/1999
• Status: Active Grant

• Current Assignee: SRA International, Inc. (General Dynamics Corporation)
• Original Assignee: Rannoch Corporation (General Dynamics Corporation)
• Inventors: Baldwin, Jonathan C., Smith, Alexander E., Evers, Carl
• Granted Patent: US 7,132,982 B2
• Time in Patent Office: Days
• US Class Current: 342/387
• CPC Class Codes: G01S 13/723 by using numerical data
• CPC Class Codes: G01S 13/781 Secondary Surveillance Radar [SSR] in general
• CPC Class Codes: G08G 5/0013 with a ground station
• CPC Class Codes: G08G 5/0082 for monitoring traffic from a ground station radar or analogous systems specially adapted for traffic control G01S13/91

Abstract

A direct multilateration target tracking system is provided with the TOA time stamp as an input. A technique of tracking targets with varying receiver combinations is provided. Methods of correlating and combining Mode A, Mode C, and Mode S messages to enhance target tracking in a passive surveillance system are provided. A direct multilateration target tracking system is provided by TOA tracking and smoothing. A technique for selecting best receiver combination and/or solution of multilateration equations from a multitude of combinations and/or solutions is provided. A technique for correcting pseudorange values with atmospheric conditions is provided. A technique for improving height determination for regions of poor VDOP in a 3D multilateration system is provided.

Claims

1. A method of tracking targets, comprising the steps of:

receiving in a plurality of sensors, a data signal from an target, the data signal including identification information,

generating, in the plurality of sensors, a time stamp TOA indicating when the data signal is received at each of the plurality of sensors,

setting a cycle end time T_end as a preset cutoff time at a central server to release a batch of queued input data to a tracking module for processing,

fetching a new TOA value,

comparing in a first comparing step, a TOA with T_end value and if the TOA value is later than the T_end value, performing a split track check and returning to the setting cycle end time step,

if the TOA value is greater than or equal to the T_end value, calculating initial solutions of 2D Mlat equations from target track data, wherein if the initial solutions for the 2D Mlat equations fail, returning to the setting cycle end time step,

if the initial solutions of the 2D Mlat equations pass, performing a data-track association test to determine whether the data-track data is associated with an existing target track, should be a new track, or should be rejected,

wherein if the data is associated with an existing track, the existing track position and velocities are filtered and updated with the latest record data, fetching a new TOA data, and returning to the setting a cycle end time step,

wherein if the data-track data is not associated with an existing track, creating a new track and returning to the setting a cycle end time step, and

wherein if outlier data is generated, the data is rejected and returning to the setting cycle time step.

2. The method of claim 1, wherein outlier data comprises a set of TOA data which is determined by the tracking system to be associated with an existing track but is suspected to suffer from some inconsistencies, and would thus not be used to update the track record.

3. The method of claim 1, wherein said step of performing a data track association test further comprises the steps of:

determining from the initial Mlat solution and TOA data whether the TOA data comprises Mode S data,

wherein if the TOA data is not Mode S data, all tracks are tested for a match,

wherein if there is no match, a new track is created,

wherein if a match exists, track position and velocities are filtered and updated, and

wherein if outlier data is detected the TOA data is rejected.

4. The method of claim 3, wherein in said step of performing a data track association test, wherein if the TOA data is Mode S data, first testing the mode S tracks for a match and then testing the ATCRBS tracks for a match.

5. The method of claim 3, wherein said step of testing the mode S tracks for a match comprises the steps of:

retrieving next track data from initial Mlat solution,

retrieving TOA data,

determining if the next track is not the final track,

wherein if the next track is not the final track, performing an altitude test,

wherein if the altitude test fails, returning to the step of retrieving next track data from initial Mlat solution, and

wherein if the altitude test passes, performing a TDOA/Position test and signal strength test.

6. The method of claim 4, wherein said step of testing the mode S tracks for a match comprises the steps of:

retrieving next track data from initial Mlat solution,

retrieving TOA data,

determining if the next track is not the final track,

wherein if the next track is not the final track, performing an altitude test,

wherein if the altitude test fails, returning to the step of retrieving next track data from initial Mlat solution, and

wherein if the altitude test passes, performing a TDOA/Position test.

7. The method of claim 5, wherein said step of performing a TDOA/Position test further comprises the steps of:

retrieving next track data from the initial Mlat solution,

retrieving TOA data,

obtaining a list of common sensors between input data and previous track data, the list including all of the sensors used in obtaining both input data and previous track data.

8. The method of claim 6, wherein said step of performing a TDOA/Position test further comprises the steps of:

retrieving next track data from the initial Mlat solution,

retrieving TOA data,

obtaining a list of common sensors between input data and previous track data, the list including all of the sensors used in obtaining both input data and previous track data.

9. The method of claim 7, wherein said step of performing a TDOA/Position test further comprises the steps of:

determining whether more than three sensors are in common,

wherein if more than three sensors are in common, performing a TDOA hypothesis test,

wherein if the TDOA hypothesis test passes the two tracks are determined to be from the same target,

wherein if the TDOA test fails, the target tracks are deemed not related.

10. The method of claim 8, wherein said step of performing a TDOA/Position test further comprises the steps of:

determining whether more than three sensors are in common,

wherein if more than three sensors are in common, performing a TDOA hypothesis test,

wherein if the TDOA hypothesis test passes the two tracks are determined to be from the same target,

wherein if the TDOA test fails, the target tracks are deemed not related.

11. The method of claim 9, wherein said step of performing a TDOA/Position test further comprises the steps of:

performing a position test to determine whether the present data and previous track are indicating the same position within a predetermined tolerance,

wherein if the position test passes, the track position and velocities are filtered and updated,

wherein if outlier data is detected, the TOA data is rejected, and

wherein if the position test fails, a signal strength comparison test is performed.

12. The method of claim 10, wherein said step of performing a TDOA/Position test further comprises the steps of:

performing a position test to determine whether the present data and previous track are indicating the same position within a predetermined tolerance,

wherein if the position test passes, the track position and velocities are filtered and updated,

wherein if outlier data is detected, the TOA data is rejected, and

wherein if the position test fails, a signal strength comparison test is performed.

13. The method of claim 11, wherein said step of performing a signal strength test further comprises the steps of:

comparing the relative signal strength of received signals is compared,

wherein if the relative signal strength of received signals is not within a certain tolerance, determining that the data-track data is not associated with an existing track, creating a new track and returning to the setting a cycle end time step, and

wherein if the relative signal strength of received signals is substantially the same strength, a hypothesis test is performed.

14. The method of claim 12, wherein said step of performing a signal strength test further comprises the steps of:

comparing the relative signal strength of received signals is compared,

wherein if the relative signal strength of received signals is not within a certain tolerance, determining that the data-track data is not associated with an existing track, creating a new track and returning to the setting a cycle end time step, and

wherein if the relative signal strength of received signals is substantially the same strength, a hypothesis test is performed.

15. The method of claim 13, wherein said step of performing a hypothesis test further comprises correlating two ATCRBS messages of different codes or an ATCRBS and a Mode S message, and the combination of them into a single target track by:

comparing two sets of message data in which a new raw multilateration solution position is calculated for one set by assuming the altitude to be the same as the other set, wherein the that changes altitude must not contain a Mode S message, and

feeding the new raw solution is into positional metric to determine whether the two sets of message data belong to the same target.

16. The method of claim 14, wherein said step of performing a hypothesis test further comprises correlating two ATCRBS messages of different codes or an ATCRBS and a Mode S message, and the combination of them into a single target track by:

comparing two sets of message data in which a new raw multilateration solution position is calculated for one set by assuming the altitude to be the same as the other set, wherein the that changes altitude must not contain a Mode S message, and

feeding the new raw solution is into positional metric to determine whether the two sets of message data belong to the same target.

17. The method of claim 15, wherein if said hypothesis test fails, determining the data-track data is not associated with an existing track, creating a new track and returning to the setting a cycle end time step, and

wherein if the hypothesis test passes, a match exists and track position and velocities are filtered and updated.

18. The method of claim 16, wherein if said hypothesis test fails, determining the data-track data is not associated with an existing track, creating a new track and returning to the setting a cycle end time step, and

wherein if the hypothesis test passes, a match exists and track position and velocities are filtered and updated.

19. The method of claim 9, wherein in said step of performing a TDOA/Position test,

if outlier data is detected, rejecting the TOA data and returning to the set cycle end time step,

if the TDOA/Position test fails, returning to the step of retrieving next track data and TOA data, and

if the TDOA/Position test passes, adding the track to a candidate list in step and returning to the step of retrieving next track data from initial Mlat solution and TOA data.

20. The method of claim 10, wherein in said step of performing a TDOA/Position test,

if outlier data is detected, rejecting the TOA data and returning to the set cycle end time step,

if the TDOA/Position test fails, returning to the step of retrieving next track data and TOA data, and

if the TDOA/Position test passes, adding the track to a candidate list in step and returning to the step of retrieving next track data from initial Mlat solution and TOA data.

21. The method of claim 5, wherein in said step of determining if the next track, is not the final track, if the final track is detected, selecting the closest track from the candidates from the step of adding the track to a candidate list

wherein if no closest track exists, then no match exists and the data-track data is not associated with an existing track, creating a new track and returning to the setting a cycle end time step,

wherein if a closest track exists, a match exists, track position and velocities are filtered and updated,

if outlier data is detected, the TOA data is rejected, and

If no match exists processing passes to step 173.

22. The method of claim 6, wherein in said step of determining if the next track, is not the final track, if the final track is detected, selecting the closest track from the candidates from the step of adding the track to a candidate list

wherein if no closest track exists, then no match exists and the data-track data is not associated with an existing track, creating a new track and returning to the setting a cycle end time step,

wherein if a closest track exists, a match exists, track position and velocities are filtered and updated,

if outlier data is detected, the TOA data is rejected, and

If no match exists processing passes to step 173.

23. The method of claim 4, wherein said step of testing ATCRBS tracks for a match comprises the steps of:

retrieving a next track data from initial the Mlat solution and TOA data.

If the retrieved track data is not the final track, performing a Mode-S track check,

wherein if the track is not mode-S, returning to said retrieving next track data step,

wherein if the track is mode-S, a Mode S address match is determined, and a TDOA/Position test is performed to determine whether the track data defines the same positions within a predetermined parameter,

wherein if the TDOA/position test fails the TOA data is rejected,

wherein if the TDOA/position test passes, a match exists, track position and velocities are filtered and updated.

24. The method of claim 23, further comprising the steps of:

If a match ATCRBS tracks exists, track target type is set to mode S and a match exists, track position and velocities are filtered and updated.

25. The method of claim 1, wherein said step of performing a split track check further comprises the steps of:

setting a top record from the track memory stack to be track 1,

determining whether any more tracks are present,

if more tracks are present, retrieving a next record below,

determining which types of track pairing is occurring, wherein track pairing determines whether two distinct track records are associated with the same target, or represent two separate target paths,

if the two tracks use different data types, performing a merger check to determine whether the two tracks are from the same target

if the two tracks are not related, fetching a next track data for further comparison

if the tracks are related to the same target, merging the two tracks,

setting the pointer to the next record of track 1 as the top of the stack,

if the next record is the only record in the stack, cleaning up the input queue,

if the other track records are in the stack, returning to the step of split track checking.

26. The method of claim 1, where in said step of calculating initial solutions of 2D Mlat equations from target track data comprises the step of:

determining the multilateration ranging equation may be written as;

{square root}{square root over ((x−

x_i)²+(y−

y_i)²+(z−

z_i)²)}=c_(t_l−

t)

where (x_i, y_i, z_i, t_i) represent local Cartesian coordinate and time of arrival at sensor number i. The vector x, y, z, t) contains the target coordinate and the time of emission,

for 2-D tracking, setting the target z-coordinate using a reported altitude, and

if a reported altitude is not available, setting a nominal z-coordinate,

A minimum of three sensors are needed for solving the remaining unknowns (x,y,t).

27. The method of claim 26, wherein said step of calculating initial solutions of 2D Mlat equations from target track data further comprises the step of:

inputting TOA data for all visible sensors, wherein visible sensor comprises sensors which receive the data signal from a target,

updating an admitted sensor list based upon input TOA data,

obtaining baseline Bancroft solutions for all admitted sensors,

if the number of sensors is equal to or less than three, terminating processing, and

if more than three sensors are present, generating four sets of three-sensor combinations from the sensor list, and generating Bancroft solutions for each combination of three sensors.

28. The method of claim 27, wherein said step of calculating initial solutions of 2D Mlat equations from target track data further comprises the step of:

testing consistency of the solutions for all sets,

if the solutions are consistent, obtaining the best HDOP solution from all the sets, where HDOP represents Horizontal dilution of precision, the approximate ratio of root-mean-squares error in the horizontal plane to the root-mean-squares error of pseudorange, where pseudorange is equal to the product of the speed of radio wave and the measured travel time between the sensor and the target.

29. The method of claim 28, wherein said step of calculating initial solutions of 2D Mlat equations from target track data further comprises the step of:

if the solutions are inconsistent, and if the number of sensors is greater than four, a residuals test is performed and the number of sensors in the admitted sensor list is downsized.

if the solutions are inconsistent, and if the number of sensors is already four, the process is terminated and the solutions rejected.

30. The method of claim 27,. wherein said step of generating four sets of three-sensor combinations from the sensor list and generating Bancroft solutions for each combination of three sensors further comprises the steps of:

inputting a baseline Mlat solution, where the three nearest sensors to an (x, y) position are used as base set,

replacing selected data from one of the three selected sensors with data from one of the remaining sensors and obtaining a new Bancroft solution from the new three sensor combination,

determining whether any other combination of sensors has not yet been tested, if so, obtaining new Bancroft solutions for other combinations, if not selecting from among the sensor combinations tested, the one with the best HDOP and creating a derivative set, where the Best HDOP solution is the one with the lowest value of HDOP among the 3-sensor subsets considered.

31. A target tracking system comprising:

a plurality of sensors for receiving a first signal from an aircraft, the first signal including an address corresponding to aircraft identification;

means, coupled to the plurality of sensors, for generating a time stamp indicating when the first signal is received at each of the plurality of sensors;

means for generating a plurality of aircraft position data by measuring differences in time of arrival of the first signal between at least two of the plurality of the sensors to create a plurality of aircraft position data for each group of at least two of the plurality of sensors; and

means for comparing the plurality of aircraft position data to determine which group of at least two of the plurality of sensors to use in tracking the target.

32. A direct multilateration 2-D target tracking system comprising:

a plurality of sensors for receiving signals from an aircraft;

means, coupled to the plurality of sensors, for generating time stamp data indicating when the signals are received at each of the plurality of sensors;

means for generating aircraft position x-y position data and velocity by multilaterating the time stamp data;

means for filtering the time stamp data using an extended Kalman Filter technique where state variables in the filter are target x-y positions and velocities, the filter based on linearized statistical target dynamics state equations and statistical measurement equations;

wherein depending on the type of target movement, the dynamics state equations may be one of the two forms;

one optimized for linear constant-speed motion, and the other optimized for targets under longitudinal or lateral accelerations,

wherein measurement equations are defined by time-difference of arrival equations, with random noise characterizing the error in TOA measurements.

33. A multilateration target tracking system using varying receiver combinations, comprising:

a plurality of sensors for receiving a first signal from an aircraft, the first signal including an address corresponding to aircraft identification;

means, coupled to the plurality of sensors, for generating a time stamp indicating when the first signal is received at each of the plurality of sensors;

a program memory for recording a sensor combination used in a previous track update process; and

determination means for determining an optimal sensor combination for a measurement equation in an extended Kalman Filter model,

wherein the determination means uses a same sensor combination as a previous measurement to improve track accuracy and smoothness,

wherein if a same sensor combination as a previous measurement cannot be found, but a common subset of at least three sensors may be found in both measurements, the common subset of sensor combination is used to calculate target position, and

wherein if a same sensor combination as a previous measurement cannot be found and a common subset of at least three sensors is not found in both measurement, a sensor combination with the lowest HDOP will be chosen.

34. A multilateration target tracking system for correlating different message modes, said system comprising:

a plurality of sensors for receiving a first signal from an aircraft, the first signal including at least one aircraft message received in one of a number of predetermined message modes;

means, coupled to the plurality of sensors, for generating a time stamp (TOA) indicating when the at least one aircraft message is received at each of the plurality of sensors;

a track memory system including track state variables and at least a selective recent history of aircraft messages and sensor data for the target, the selective recent history including at least one or more of TOA with sensor identification, signal strengths, and a list of sensors selected to contribute to tracking solutions,

wherein stored aircraft messages in the track memory system are retrieved and compared with new aircraft target messages to correlate different messages in different message modes to a single target.

35. A direct multilateration 2-D target tracking system comprising:

a plurality of sensors for receiving signals from an aircraft;

means, coupled to the plurality of sensors, for generating time stamp data indicating when the signals are received at each of the plurality of sensors;

means for generating aircraft position x-y position data and velocity by multilaterating the time stamp data; and

means for filtering the time stamp data using an extended Kalman Filter technique where state variables in the filter are pseudoranges and x-y velocities as state variables in an extended Kalman filtering model.

36. A direct multilateration 2-D target tracking system comprising:

a plurality of sensors for receiving a first signal from an aircraft, the first signal including an address corresponding to aircraft identification;

means, coupled to the plurality of sensors, for generating a time stamp indicating when the first signal is received at each of the plurality of sensors;

means for selecting at least one of a best receiver combination and solution of multilateration equations.

37. A multilateration target tracking system comprising:

a plurality of sensors for receiving a first signal from an aircraft, the first signal including an address corresponding to aircraft identification;

means, coupled to the plurality of sensors, for generating a time stamp indicating when the first signal is received at each of the plurality of sensors;

means for correcting pseudorange values with atmospheric conditions, including;

means for obtaining an initial Mlat solution from time stamp data to calculate initial raw multilateration solutions without regards to atmospheric conditions,

means for estimating initial time stamp correction factors based on the knowledge of atmospheric profile between each sensor and apparent target locations,

means for adding initial time stamp correction factors to the time stamp data, and

deriving nonlinear equation relating true target height with apparent target height at a given range,

wherein the nonlinear equation is solved using the apparent target location as input, for what would be true height and range as seen by each sensor.

38. The multilateration target tracking system of claim 37, further comprising:

means for comparing the preliminary true height and range information and the apparent target range information and estimating the time stamp correction term for each sensor,

means for applying the time stamp correction term to each sensor, and

means for calculating a new Mlat solution to pinpoint the true target position without correction for refractions.

39. The multilateration target tracking system of claim 38, further comprising:

means for calculating real propagation delay T_d by comparing the new Mlat solution with the initial Mlat solution, wherein if t_d is close to the unmodified pseudorange, then the solution is considered found and if not, new time stamp corrections are computed.

40. A 3-D multilateration target tracking system comprising:

a plurality of sensors for receiving a first signal from an aircraft, the first signal including an address corresponding to aircraft identification;

means, coupled to the plurality of sensors, for generating a time stamp indicating when the first signal is received at each of the plurality of sensors;

means for improving height determination for regions of poor VDOP in a 3D multilateration system is provided. In a 3D multilateration system, optimum HDOP and optimum VDOP may not occur at the same locations and may be coming from the same sensor combination. This makes it more difficult to achieve consistent 3D tracking performance, especially for height determination applications.

a height determination algorithm for obtaining better vertical accuracy in places with good HDOP, the algorithm is built upon a 2D tracking system, taking advantage of the altitude information contained in Mode C and Mode S messages to make possible robust 2D tracking of horizontal positions,

wherein for any given time, a modified set of 3D multilateration equations is formed by setting the x-y position of the target with that given by the 2D tracking system, obtaining a solution of the unknown altitude by a least-squares technique.

41. A direct multilateration target tracking system comprising:

a plurality of sensors for receiving signals from an aircraft;

means, coupled to the plurality of sensors, for generating time stamp data indicating when the signals are received at each of the plurality of sensors;

means for generating aircraft position x-y position data and velocity by multilaterating the time stamp data; and

means for selecting at least one of a best receiver combination and solution of multilateration equations from a multitude of combinations and solutions.

42. The target tracking system of claim 41, wherein said means for selecting at least one of a best receiver combination and solution of multilateration equations from a multitude of combinations and solutions further comprises:

means for performing error detection and correction I such that when more than four sensors present TOAs for the same target, an algorithm checks the consistencies among solutions generated by selective sensor combination such that if inconsistency is found, further tests are done individually to determine which sensor should not contribute to the final solution.

43. The target tracking system of claim 41, wherein when only four sensors present TOAs for the same target, the consistency checking algorithm indicates an error and the message will not be used for track update if inconsistency in TOAs is detected.

44. The target tracking system of claim 41, wherein said means for selecting at least one of a best receiver combination and solution of multilateration equations from a multitude of combinations and solutions further comprises:

means for perform ambiguity resolution and false target elimination comprising;

means for taking a majority-vote among data coming at different times with different sensor combinations such that although each sensor combination may yield two valid solutions, a true target position centers around solutions taken by majority votes.

45. The target tracking system of claim 41, wherein said means for selecting at least one of a best receiver combination and solution of multilateration equations from a multitude of combinations and solutions further comprises:

means for perform ambiguity resolution and false target elimination comprising;

means for tracking target speeds from selected sensors, wherein sensor data combination solutions generating a target speed that falls substantially outside the reasonable range of speed for targets that normally appear in those locations are discarded.