Track prediction and identification via particle motion with intent

• US 9,002,642 B2
• Filed: 06/04/2008
• Issued: 04/07/2015
• Est. Priority Date: 06/04/2008
• Status: Active Grant

• Current Assignee: Raytheon Company (Rtx Corporation)
• Original Assignee: Raytheon Company (Rtx Corporation)
• Inventors: Noble, William B., Gokcen, Serdar N., Howard, Michael D., Payton, David W.
• Primary Examiner(s): COUPE, ANITA YVONNE
• Application Number:
  US12/156,686
• Publication Number:
  US 20090306894A1
• Time in Patent Office: 2,498 Days
• Field of Search: None
• US Class Current: 701/519
• CPC Class Codes: G01S 13/723 by using numerical data
• CPC Class Codes: G01S 13/878 Combination of several spaced transmitters or receivers of known location for determining the position of a transponder or a reflector G01S13/874 takes precedence
• CPC Class Codes: G01S 7/415 Identification of targets based on measurements of movement associated with the target
• CPC Class Codes: G06Q 10/08355 Routing methods

Abstract

Provided is a system and method for tracking and identifying a target in an area of interest based on a comparison of predicted target behavior or movement and sensed target behavior or movement. Incorporating aspects of both particle diffusion and mobility constraint models with target intent derivations, the system may continuously track a target while simultaneously refining target identification information. Alternatively, the system and method are applied to reacquire a target track based on prioritized intents and predicted target location.

Claims

1. A method of tracking a target executed by one or more processors, comprising:

sensing, by a sensor, a target in an area of interest, the target having a type and the type having at least one mobility constraint associated therewith;

creating, by one or more processors, a set of particles to represent the sensed target;

attributing, by the one or more processors, to each particle a functional intent, wherein a probability density of the particles in the set of particles correlates to a probability the density of possible intents that guide movements of the sensed target, and wherein the functional intent of a specific particle is a combination of behavioral goals of a person guiding the sensed target and the mobility constraint of the type of the sensed target;

grouping, by the one or more processors, the set of particles based on attributed functional intents;

defining at least one vehicle type having predetermined vehicle characteristics;

identifying, by the one or more processors, objects within the area of interest with which the sensed target may interact;

allocating, by the one or more processors, to each object an attraction-repulsion factor for each functional intent;

correlating, by the one or more processors, functional intents, vehicle types, vehicle characteristics, and objects having attraction-repulsion factors with defined mobility constraints for the area of interest, to define a rule of behavior for each particle having a particular intent, wherein said rule of behavior defines a relationship between the functional intent of a particle and the objects within the area of interest;

applying, by the one or more processors, said rule of behavior to each particle to predict a movement and geographic location for each particle representing the sensed target; and

moving, by the one or more processors, each particle representing a predicted location of the target in accordance with the observed movements of the sensed target, said rule of behavior applied to each particle and geographic location for each particle.

2. The method of claim 1, further comprising categorizing, by the one or more processors, particles of like functional intent as a target-of-interest.

3. The method of claim 2, further comprising:

pruning particles not categorized as a target-of-interest, by the one or more processors;

creating, by the one or more processors, additional particles such that the number of particles pruned and the number of particles additionally created are equal; and

distributing, by the one or more processors, created particles to modify functional intent probabilities.

4. The method of claim 1, further comprising comparing, by the one or more processors, the predicted movement and geographic location for each particle of like functional intent to a known movement and geographic location for the target to identify the functional intent of the target.

5. The method of claim 1, wherein the target is land based.

6. The method of claim 1, wherein sensor coverage of the target is discontinuous, and wherein a reacquisition of the tracked target is desired.

7. The method of claim 6, further comprising:

establishing a priority for each functional intent; and

initiating, by the one or more processors, a reacquisition of the target based on functional intent priorities.

8. The method of claim 1, further comprising:

selecting a goal for each particle representing a functional intent; and

applying, by the one or more processors, mobility constraints and attraction-repulsion factors to define at least one route for reaching the goal.

9. The method of claim 8, further comprising optimizing the route, by the one or more processors.

10. The method of claim 8, further comprising incorporating a probability that a particle will divert from a chosen goal to a different goal, by the one or more processors.

11. The method of claim 1, further comprising apportioning to each particle a selected behavior, by the one or more processors.

12. The method of claim 11, wherein the selected behavior is chosen from a group consisting of:

speed, route diversion, and hazard avoidance.

13. A system for tracking and identifying a target, comprising:

at least one sensor for sensing the target in an area of interest, the target having a type and the type having at least one mobility constraint associated therewith;

a first database residing on a first computer storage medium and including a plurality of vehicle types and corresponding vehicle characteristics;

a second database residing on a second computer storage medium and including a set of particles to represent the sensed target and a plurality of functional intents attributed to each vehicle type, wherein the set of particles are grouped to represent the sensed target, and wherein a probability density of the particles in the set of particles correlates to a probability the density of possible intents that guide movements of the sensed target, and wherein the functional intent of a specific particle is a combination of behavioral goals of a person guiding the sensed target and the mobility constraint of the type of the sensed target;

a third database residing on a third computer storage medium and populated with objects in the area of interest having a predetermined attraction-repulsion factor, wherein the attraction-repulsion factor is related to a relationship between a given functional intent and a given object; and

a processor

for correlating functional intents, vehicle types, vehicle characteristics, and objects having attraction-repulsion factors in the first, second, and third databases with pre-determined mobility constraints for the area of interest;

for defining a rule of behavior for each particle having a particular intent, wherein said rule of behavior defines a relationship between the functional intent of a particle and the objects within the area of interest;

for applying said rule of behavior to each particle to predict a movement and geographic location for each particle representing the sensed target; and

for moving each particle representing a predicted location of the target in accordance with the observed movements of the sensed target, said rule of behavior applied to each particle and geographic location for each particle.

14. The system of claim 13 wherein the at least one sensor covers the target in a discontinuous manner, and wherein the track of the target is initiated using predetermined priorities for each functional intent.

15. The system of claim 13, wherein the processor further performs a pruning function for eliminating particles.

16. The system of claim 13, further comprising one or more finite state machines.

17. The system of claim 16, wherein the one or more finite state machines target behaviors selected from a group consisting of:

goal achievement, goal modification, target speed, route diversion, and hazard avoidance.

18. The system of claim 13, wherein the target is selected from one or more of a group consisting of:

at least one land based target, at least one sea based target, at least one air based target, and at least one space based target.