Method and system for multi-sensor data fusion

• US 7,065,465 B2
• Filed: 03/25/2003
• Issued: 06/20/2006
• Est. Priority Date: 03/26/2002
• Status: Expired due to Fees

• Current Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Original Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Inventors: Chen, Hai-Wen, Olson, Teresa L.
• Primary Examiner(s): Knight, Anthony
• Assistant Examiner(s): Holmes, Michael B.
• Application Number:
  US10/395,265
• Publication Number:
  US 20030186663A1
• Time in Patent Office: 1,183 Days
• Field of Search: 455/226.3, 455/205, 702/189, 702/116, 702/190, 702/191, 702/192, 702/193, 324/614
• US Class Current: 702/116
• CPC Class Codes: G06F 18/25 Fusion techniques
• CPC Class Codes: G06F 18/251 of input or preprocessed data
• CPC Class Codes: G06F 18/256 of results relating to different input data, e.g. multimodal recognition
• CPC Class Codes: G06T 2207/10048 Infrared image
• CPC Class Codes: G06T 5/40 using histogram techniques
• CPC Class Codes: G06T 5/92 based on global image properties

Abstract

A multi-sensor data fusion system and method provides adaptive weighting of the contributions from a plurality of sensors in the system using an additive calculation of a sensor reliability function for each sensor. During a predetermined tracking period, data is received from each individual sensor in the system and a sensor reliability function is determined for each sensor based on the SNR (signal-to-noise ratio) for the received data from each sensor. Each sensor reliability function is individually weighted based on the SNR for each sensor and a comparison of predetermined sensor operation characteristics for each sensor and a best performing (most reliable) sensor. Additive calculations are performed on the sensor reliability functions to produce both an absolute and a relative reliability function which provide a confidence level for the multi-sensor system relating to the correct classification (recognition) of targets and decoys.

Claims

1. A method for integrating data received from a plurality of sensors, comprising:

receiving data from a plurality of sensors;

determining a SNR (signal-to-noise ratio) for each sensor based on signal measurements of the received data; and

determining at least one reliability function for the plurality of sensors as a predetermined additive calculation of each sensor reliability function which are determined and individually weighted based on said SNR for each sensor and differences between predetermined operating characteristics for each sensor.

2. The method of claim 1, wherein said determining at least one reliability function includes determining at least one of an absolute reliability and a relative reliability function.

3. The method of claim 1, wherein said receiving includes receiving data, during a predetermined time period, over a plurality of time frames from said plurality of sensors.

4. The method of claim 1, wherein said determining at least one reliability function includes determining said individual weighting based on a predetermined parameter for the plurality of sensors satisfying a predetermined threshold, and determining said at least one reliability function as a predetermined additive calculation of a single sensor reliability function when said predetermined parameter fails to satisfy said predetermined threshold.

5. The method of claim 4, wherein said predetermined parameter is a false alarm rate for the plurality of sensors.

6. The method of claim 1, wherein said determining at least one reliability function includes determining said at least one reliability function based on differences between probabilities of classification for each sensor as compared to a single sensor having the highest probability of classification.

7. The method of claim 1, wherein said plurality of sensors includes at least one of a laser, IR (infrared), and RF (radio frequency) sensor.

8. The method of claim 1, wherein said determining at least one reliability function includes determining said at least one reliability function based on said predetermined additive and one of predetermined multiplicative, and fuzzy logic calculation of each said sensor reliability function.

9. The method of claim 8, wherein said determining at least one reliability function includes determining empty sets and ignorance sets for the received data from the plurality of sensors based on said predetermined multiplicative calculation.

10. The method of claim 9, wherein said predetermined multiplicative calculation includes at least a modified portion of a Dempster'"'"'s data fusion algorithm.

11. The method of claim 1, wherein said determining at least one reliability function includes determining said at least one reliability function based on determining a single sensor, as compared to the plurality of sensors, as having the best performance for at least one predetermined sensor parameter.

12. The method of claim 11, wherein said at least one predetermined sensor parameter is one of operating characteristics and SNR for said single sensor.

13. The method of claim 11, wherein said single sensor is one of an IR and RF sensor.

14. The method of claim 1, wherein said determining at least one reliability function includes determining said at least one reliability levels to increase the probability of classifying one of a target and decoy above a predetermined threshold.

15. A method for integrating data received from a plurality of sensors, comprising:

receiving data from a plurality of sensors;

determining a SNR (signal-to-noise ratio) for each sensor based on signal measurements of the received data; and

determining at least one reliability function for the plurality of sensors as a predetermined additive calculation of each sensor reliability function which are determined and individually weighted based on said SNR for each sensor.

16. A method for integrating data received from a plurality of sensors, comprising:

receiving data from a plurality of sensors;

determining at least one reliability function for the plurality of sensors as a predetermined additive calculation of each sensor reliability function which are determined and individually weighted based on differences between predetermined operating characteristics for each sensor.

17. A multi-sensor system, comprising:

a plurality of sensors for receiving data; and

a controller for performing the steps of;

determining a SNR (signal-to-noise ratio) for each sensor based on signal measurements of the received data; and

determining at least one reliability function for the plurality of sensors as a predetermined additive calculation of each sensor reliability function which are determined and individually weighted based on said SNR for each sensor and differences between predetermined operating characteristics for each sensor.

18. The system of claim 17, wherein said controller to determine said at least one reliability function including one of an absolute reliability and a relative reliability function.

19. The system of claim 17, wherein said controller to determine said individual weighting based on a predetermined parameter for the plurality of sensors satisfying a predetermined threshold, and said controller to determine said at least one reliability function as a predetermined additive calculation of a single sensor reliability function when said predetermined parameter fails to satisfy said predetermined threshold.

20. The system of claim 19, wherein said predetermined parameter is a false alarm rate for the plurality of sensors.

21. The system of claim 17, wherein said plurality of sensors includes at least one of a laser, IR (infrared), and RF (radio frequency) sensor.

22. A machine-readable medium having stored thereon a plurality of executable instructions, the plurality of instructions comprising instructions to:

receive data from a plurality of sensors;

determine a SNR (signal-to-noise ratio) for each sensor based on signal measurements of the received data; and

determine at least one reliability function for the plurality of sensors as a predetermined additive calculation of each sensor reliability function which are determined and individually weighted based on said SNR for each sensor and differences between predetermined operating characteristics for each sensor.

23. The medium of claim 22, wherein said instructions to determine at least one reliability function include instructions to determine one of an absolute and a relative reliability function.

24. The medium of claim 22, wherein said instructions to determine at least one reliability function include instructions to determine said individual weighting based on a predetermined parameter for the plurality of sensors satisfying a predetermined threshold.

25. The medium of claim 22, wherein said instructions to determine at least one reliability function include instructions to determine said at least one reliability function based on differences between probabilities of classification for each sensor as compared to a single sensor having the highest probability of classification.

26. The medium of claim 22, wherein said instructions to determine at least one reliability function include instructions to determine said at least one reliability function based on said predetermined additive and one of predetermined multiplicative, minimum, and maximum calculation of each sensor reliability function.

27. The medium of claim 22, wherein said instructions to determine at least one reliability function include instructions to determine said at least one reliability function to increase the probability of classifying one of a target and a decoy above a predetermined threshold.