UPDATING INTENSITIES IN A PHD FILTER BASED ON A SENSOR TRACK ID

• US 20160033281A1
• Filed: 07/31/2014
• Published: 02/04/2016
• Est. Priority Date: 07/31/2014
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Bageshwar, Vibhor L., Elgersma, Michael Ray, Euteneuer, Eric A.
• Granted Patent: US 11,175,142 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01C 21/20 Instruments for performing navigational calculations G01C21/24, G01C21/26 take precedence
• CPC Class Codes: G01D 9/30 there being a separate recording element for each variable, e.g. multiple-pen recorder
• CPC Class Codes: G01D 9/32 there being a common recording element for two or more variables
• CPC Class Codes: G01S 13/726 Multiple target tracking
• CPC Class Codes: G01S 13/86 Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
• CPC Class Codes: G01S 13/865 Combination of radar systems with lidar systems
• CPC Class Codes: G01S 13/867 Combination of radar systems with cameras
• CPC Class Codes: G01S 13/933 of aircraft or spacecraft

Abstract

In one embodiment, a method of tracking multiple objects with a probabilistic hypothesis density filter is provided. The method includes obtaining measurements corresponding to a first object with at least one sensor, the at least one sensor providing one or more first track IDs for the measurements. A T_k+1 first predicted intensity is generated for the first object based on a T_k first track intensity. A T_k+1 measurement from a first sensor of the at least one sensors is obtained, the first sensor providing a second track ID for the T_k+1 measurement. The second track ID is compared to the one or more first track IDs, and the T_k+1 first predicted intensity is selectively updated with the T_k+1 measurement based on whether the second track ID matches any of the one or more first track IDs to generate a T_k+1 first measurement-to-track intensity for the first object.

Claims

1. A method of tracking multiple objects with a probabilistic hypothesis density filter, the method comprising:

obtaining a plurality of measurements corresponding to a first object with at least one sensor, the at least one sensor providing one or more first track IDs for the plurality of measurements;

generating a T_k first track intensity for the first object based on the plurality of measurements, the T_k first track intensity including a weight, a state mean vector, and a state covariance matrix of statistics of a track of the first object at time T_k;

generating a T_k+1 first predicted intensity for the first object based on the T_k first track intensity, the T_k+1 first predicted intensity corresponding to time T_k+1;

obtaining a T_k+1 measurement from a first sensor of the at least one sensors, the first sensor providing a second track ID for the T_k+1 measurement, wherein the T_k+1 measurement corresponds to time T_k+1;

comparing the second track ID to the one or more first track IDs; and

selectively updating the T_k+1 first predicted intensity with the T_k+1 measurement based on whether the second track ID matches any of the one or more first track IDs to generate a T_k+1 first measurement-to-track intensity for the first object at time T_k+1.

2. The method of claim 1, comprising:

computing a statistical distance between the T_k+1 first predicted intensity and the T_k+1 measurement; and

wherein selectively updating includes selectively updating the T_k+1 first predicted intensity with the T_k+1 measurement based on whether the statistical distance is less than a threshold.

3. The method of claim 2, wherein selectively updating includes:

if the second track ID matches any of the one or more first track IDs and the statistical distance is less than a threshold, updating the T_k+1 first predicted intensity with the T_k+1 measurement; and

if either the second track ID does not match any of the one or more first track IDs or the statistical distance is greater than the threshold, not updating the T_k+1 first predicted intensity with the T_k+1 measurement.

4. The method of claim 1, comprising:

generating a plurality of T_k track intensities including the T_k first track intensity, the plurality of T_k track intensities having a plurality of track IDs associated therewith, the plurality of track IDs including the first one or more track IDs,

if the second track ID does not match any of the plurality of track IDs, generating a new intensity having values equal to the T_k+1 measurement.

5. The method of claim 4, comprising:

generating a T_k+1 second track intensity for a second object based on the new intensity, the T_k+1 second track intensity corresponding to time T_k+1;

generating a T_k+2 second predicted intensity based on the T_k+1 second track intensity, the T_k+2 second predicted intensity corresponding to time T_k+2;

associating the second track ID with the T_k+2 second predicted intensity;

obtaining a T_k+2 measurement from the first sensor, the first sensor providing a third track ID for the T_k+2 measurement, wherein the T_k+2 measurement corresponds to time T_k+2;

comparing the third track ID to the second track ID;

computing a second statistical distance between the T_k+2 second predicted intensity and the T_k+2 measurement;

selectively updating the T_k+2 second predicted intensity with the T_k+2 measurement based on whether the third track ID matches the second track ID and based on whether the second statistical distance is less than the threshold to generate a T_k+2 second measurement-to-track intensity for the second object at time T_k+2.

6. The method of claim 1, comprising:

generating a T_k+1 first track intensity based on the T_k+1 first measurement-to-track intensity;

associating the one or more first track IDs with the T_k+1 first track intensity;

generating a T_k+2 first predicted intensity for the first object based on the T_k+1 first track intensity, the T_k+2 first predicted intensity corresponding to time T_k+2;

obtaining a T_k+2 measurement from the first sensor, the first sensor providing a fourth track ID for the T_k+2 measurement, wherein the T_k+2 measurement corresponds to time T_k+2;

comparing the fourth track ID to the one or more first track IDs; and

selectively updating the T_k+2 first predicted intensity with the T_k+2 measurement based on whether the fourth track ID matches any of the one or more first track IDs to generate a T_k+2 first track intensity for the first object at time T_k+2.

7. The method of claim 1, wherein selectively updating includes:

computing a statistical distance between the T_k+1 first predicted intensity and the T_k+1 measurement,

wherein selectively updating includes, if the second track ID matches any of the one or more first track IDs and the statistical distance is less than a threshold;

updating the T_k+1 first predicted intensity with the T_k+1 measurement to generate a T_k+1 first measurement-to-track intensity; and

associating only the second track ID of the one or more first track IDs with the T_k+1 first measurement-to-track intensity;

pruning the T_k+1 first predicted intensity such that the T_k+1 first predicted intensity is no longer used for tracking the first object; and

generating a T_k+1 track intensity from the T_k+1 first measurement-to-track intensity; and

associating only the second track ID of the one or more first track IDs with the T_k+1 track intensity.

8. The method of claim 1, wherein each track ID of the one or more first track IDs and the second track ID is one of an identifier assigned by a sensor to a set of measurements obtained by that sensor that are correlated across time, or an international civil aviation organization (ICAO) aircraft address received by a sensor and corresponding to a respective measurement for that sensor.

9. A tracking system comprising:

one or more processing devices; and

one or more data storage devices including instructions which, when executed by the one or more processing devices, cause the one or more processing devices to track multiple objects with a probabilistic hypothesis density filter, wherein the instructions cause the one or more processing devices to;

obtain a plurality of measurements corresponding to a first object with at least one sensor, the at least one sensor providing one or more first track IDs for the plurality of measurements;

generate a T_k first track intensity for the first object based on the plurality of measurements, the T_k first track intensity including a weight, a state mean vector, and a state covariance matrix of statistics of a track of the first object at time T_k;

generate a T_k+1 first predicted intensity for the first object based on the T_k first track intensity, the T_k+1 first predicted intensity corresponding to time T_k+1;

obtain a T_k+1 measurement from a first sensor of the at least one sensors, the first sensor providing a second track ID for the T_k+1 measurement, wherein the T_k+1 measurement corresponds to time T_k+1;

compare the second track ID to the one or more first track IDs; and

selectively update the T_k+1 first predicted intensity with the T_k+1 measurement based on whether the second track ID matches any of the one or more first track IDs to generate a T_k+1 first measurement-to-track intensity for the first object at time T_k+1.

10. The tracking system of claim 9, wherein the instructions cause the one or more processing devices to:

compute a statistical distance between the T_k+1 first predicted intensity and the T_k+1 measurement; and

wherein selectively update includes selectively update the T_k+1 first predicted intensity with the T_k+1 measurement based on whether the statistical distance is less than a threshold.

11. The tracking system of claim 10, wherein selectively update includes:

if the second track ID matches any of the one or more first track IDs and the statistical distance is less than a threshold, update the T_k+1 first predicted intensity with the T_k+1 measurement; and

if either the second track ID does not match any of the one or more first track IDs or the statistical distance is greater than the threshold, not updating the T_k+1 first predicted intensity with the T_k+1 measurement.

12. The tracking system of claim 9, wherein the instructions cause the one or more processing devices to:

generate a plurality of T_k track intensities including the T_k first track intensity, the plurality of T_k track intensities having a plurality of track IDs associated therewith, the plurality of track IDs including the first one or more track IDs,

if the second track ID does not match any of the plurality of track IDs, generate a new intensity having values equal to the T_k+1 measurement.

13. The tracking system of claim 12, wherein the instructions cause the one or more processing devices to:

generate a T_k+1 second track intensity for a second object based on the new intensity, the T_k+1 second track intensity corresponding to time T_k+1;

generate a T_k+2 second predicted intensity based on the T_k+1 second track intensity, the T_k+2 second predicted intensity corresponding to time T_k+2;

associate the second track ID with the T_k+2 second predicted intensity;

obtain a T_k+2 measurement from the first sensor, the first sensor providing a third track ID for the T_k+2 measurement, wherein the T_k+2 measurement corresponds to time T_k+2;

compare the third track ID to the second track ID;

compute a second statistical distance between the T_k+2 second predicted intensity and the T_k+2 measurement;

selectively update the T_k+2 second predicted intensity with the T_k+2 measurement based on whether the third track ID matches the second track ID and based on whether the second statistical distance is less than the threshold to generate a T_k+2 second measurement-to-track intensity for the second object at time T_k+2.

14. The tracking system of claim 9, wherein the instructions cause the one or more processing devices to:

generate a T_k+1 first track intensity based on the T_k+1 first measurement-to-track intensity;

associate the one or more first track IDs with the T_k+1 first track intensity;

generate a T_k+2 first predicted intensity for the first object based on the T_k+1 first track intensity, the T_k+2 first predicted intensity corresponding to time T_k+2;

obtain a T_k+2 measurement from the first sensor, the first sensor providing a fourth track ID for the T_k+2 measurement, wherein the T_k+2 measurement corresponds to time T_k+2;

compare the fourth track ID to the one or more first track IDs; and

selectively update the T_k+2 first predicted intensity with the T_k+2 measurement based on whether the fourth track ID matches any of the one or more first track IDs to generate a T_k+2 first track intensity for the first object at time T_k+2.

15. The tracking system of claim 9, wherein selectively update includes:

compute a statistical distance between the T_k+1 first predicted intensity and the T_k+1 measurement,

wherein selectively update includes, if the second track ID matches any of the one or more first track IDs and the statistical distance is less than a threshold;

update the T_k+1 first predicted intensity with the T_k+1 measurement to generate a T_k+1 first measurement-to-track intensity; and

associate only the second track ID of the one or more first track IDs with the T_k+1 first measurement-to-track intensity;

prune the T_k+1 first predicted intensity such that the T_k+1 first predicted intensity is no longer used for tracking the first object; and

generate a T_k+1 track intensity from the T_k+1 first measurement-to-track intensity; and

associate only the second track ID of the one or more first track IDs with the T_k+1 track intensity.

16. The tracking system of claim 9, wherein each track ID of the one or more first track IDs and the second track ID is one of an identifier assigned by a sensor to a set of measurements obtained by that sensor that are correlated across time, or an international civil aviation organization (ICAO) aircraft address received by a sensor and corresponding to a respective measurement for that sensor.

17. A computer readable medium including instructions which, when executed by one or more processing devices, cause the one or more processing devices to:

obtain a plurality of measurements corresponding to a first object with at least one sensor, the at least one sensor providing one or more first track IDs for the plurality of measurements;

generate a T_k first track intensity for the first object based on the plurality of measurements, the T_k first track intensity including a weight, a state mean vector, and a state covariance matrix of statistics of a track of the first object at time T_k;

generate a T_k+1 first predicted intensity for the first object based on the T_k first track intensity, the T_k+1 first predicted intensity corresponding to time T_k+1;

obtain a T_k+1 measurement from a first sensor of the at least one sensors, the first sensor providing a second track ID for the T_k+1 measurement, wherein the T_k+1 measurement corresponds to time T_k+1;

compare the second track ID to the one or more first track IDs; and

selectively update the T_k+1 first predicted intensity with the T_k+1 measurement based on whether the second track ID matches any of the one or more first track IDs to generate a T_k+1 first measurement-to-track intensity for the first object at time T_k+1.

18. The computer readable medium of claim 17, wherein the instructions cause the one or more processing devices to:

compute a statistical distance between the T_k+1 first predicted intensity and the T_k+1 measurement; and

wherein selectively update includes selectively update the T_k+1 first predicted intensity with the T_k+1 measurement based on whether the statistical distance is less than a threshold.

19. The computer readable medium of claim 17, wherein the instructions cause the one or more processing devices to:

generate a plurality of T_k track intensities including the T_k first track intensity, the plurality of T_k track intensities having a plurality of track IDs associated therewith, the plurality of track IDs including the first one or more track IDs,

if the second track ID does not match any of the plurality of track IDs, generate a new intensity having values equal to the T_k+1 measurement.

20. The computer readable medium of claim 17, wherein each track ID of the one or more first track IDs and the second track ID is one of an identifier assigned by a sensor to a set of measurements obtained by that sensor that are correlated across time, or an international civil aviation organization (ICAO) aircraft address received by a sensor and corresponding to a respective measurement for that sensor.