Multiple sensor fusion engine

• US 7,974,814 B2
• Filed: 06/15/2007
• Issued: 07/05/2011
• Est. Priority Date: 06/15/2007
• Status: Expired due to Fees

• Current Assignee: Raytheon Company (Rtx Corporation)
• Original Assignee: Raytheon Company (Rtx Corporation)
• Inventors: Phelps, Ethan, Landau, Herbert
• Primary Examiner(s): Raymond; Edward
• Application Number:
  US11/818,651
• Publication Number:
  US 20100070238A1
• Time in Patent Office: 1,481 Days
• Field of Search: 702/117, 702/118, 702181-185, 702/188
• US Class Current: 702/181
• CPC Class Codes: F41G 3/02 using an independent line of sight
• CPC Class Codes: F41G 7/2206 using a remote control station
• CPC Class Codes: F41G 7/30 Command link guidance systems homing F41G7/2206
• CPC Class Codes: F41H 11/02 Anti-aircraft or anti-guided missile or anti-torpedo defence installations or systems cartridges or missiles for producing smoke or for dispensing radar chaff or infrared material F42B5/15, F42B12/48, F42B12/70; informative reference: details of radar or sonar systems G01S7/00; jamming or electronic countermeasures G01S7/38, G01S7/495, H04K3/00
• CPC Class Codes: G01S 13/726 Multiple target tracking

Abstract

A method and system for of fusing the outputs of multiple sensors. The output of each sensor including a sensor class probability vector based on each sensor'"'"'s classification database is input to a fusion engine which estimates a base class probability vector based on the sensor class probabilities output by the sensors and a preconfigured base database including base classes.

Claims

1. A method of fusing the outputs of multiple sensors, the method comprising:

inputting to a fusion engine the output of each sensor including a sensor class probability vector based on each sensor'"'"'s classification database; and

estimating, using a microprocessor, a base class probability vector based on the sensor class probabilities output by the sensors and a mapping function.

2. The method of claim 1 in which estimating includes converting the sensor class probability vector to log-space.

3. The method of claim 2 in which the mapping function includes anchor points and estimating includes finding the differences between the log-space probability vectors and the anchor points.

4. The method of claim 3 in which the mapping function includes mapping matrices and estimating includes mapping each difference using the mapping matrices.

5. The method of claim 4 in which the mapping function includes reference points and estimating includes adding reference points to the mapped differences.

6. The method of claim 5 in which estimating includes weighting and summing the mapped differences.

7. The method of claim 6 in which estimating includes converting the weighted and summed mapped differences back to probability space to produce the probability vector.

8. The method of claim 7 in which the estimating includes normalizing the probability vector.

9. A system for fusing the output of multiple sensors, the system comprising:

multiple sensors each including a classification database and each outputting a sensor class probability vector based on its classification database;

a fusion engine responsive to the output of each sensor; and

a mapping function;

the fusion engine configured to estimate a base class probability vector based on the sensor outputs and the base database classes.

10. The system of claim 9 in which the fusion engine is configured to convert the sensor class probability vectors to log-space.

11. The system of claim 10 in which the mapping function includes anchor points and the fusion engine is configured to find the differences between the log-space probability vectors and the anchor points.

12. The system of claim 11 in which the mapping function includes mapping matrices and the fusion engine is configured to map each difference using the mapping matrices.

13. The system of claim 12 in which the mapping function includes reference points and the fusion engine is configured to add reference points to the mapped differences.

14. The system of claim 13 in which the fusion engine is configured to weight and sum the mapped differences.

15. The system of claim 14 in which the fusion engine is configured to convert the weighted and summed mapped differences back to probability space to produce the probability vector.

16. The system of claim 15 in which the fusion engine is configured to normalize the probability vector.