TARGET DETECTION SIGNAL PROCESSOR BASED ON A LINEAR LOG LIKELIHOOD RATIO ALGORITHM USING A CONTINUUM FUSION METHODOLOGY

• US 20130311137A1
• Filed: 05/16/2013
• Published: 11/21/2013
• Est. Priority Date: 05/16/2012
• Status: Abandoned Application

• Current Assignee: Alen Schaum, Brian J. Daniel
• Original Assignee: Alen Schaum, Brian J. Daniel
• Inventors: SCHAUM, ALAN, Daniel, Brian J.
• Application Number:
  US13/895,593
• Publication Number:
  US 20130311137A1
• Time in Patent Office: Days
• US Class Current: 702/181
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06F 18/00 Pattern recognition
• CPC Class Codes: G06F 2218/12 Classification; Matching

Abstract

A method including collecting physical measurement data from a sensor. The physical measurement data is converted to radiance data. The radiance data includes a plurality of radiance data points. A detection score is generated by processing the radiance data using a discriminant function. The detection score includes a plurality of detection score points corresponding to the plurality of radiance data points. The discriminant function is derived by a fusion technique using a linear log likelihood ratio principle. A detection map is generated by applying a threshold to the detection score. The detection map includes a plurality of detection map points corresponding to the plurality of radiance data points, each detection map point of the plurality of detection map points includes one of a target-indicating value and a clutter-indicating value. A presence or an absence of a target is determined from the detection map.

Claims

1. A method comprising:

collecting physical measurement data from a sensor;

converting the physical measurement data to radiance data, the radiance data comprising a plurality of radiance data points, each radiance data point of the plurality of radiance data points comprising a multi-dimensional vector;

generating a detection score by processing the radiance data using a discriminant function, the detection score comprising a plurality of detection score points corresponding to the plurality of radiance data points, each detection score point of the plurality of detection score points comprising a target-likelihood value, the discriminant function derived from a linear log likelihood ratio principle;

generating a detection map by applying a threshold to the detection score, the detection map comprising a plurality of detection map points corresponding to the plurality of radiance data points, each detection map point comprising one of a target-indicating value and a clutter-indicating value; and

determining from the detection map one of a presence and an absence of a target.

2. The method according to claim 1, wherein the discriminant function is represented by a curved decision surface separating at least one target from clutter.

3. The method according to claim 1, wherein said generating a detection score by processing the radiance data using a discriminant function comprises whitening the radiance data.

4. The method according to claim 3, wherein said whitening the radiance data comprises one of whitening with global statistics, whitening with local statistics, and whitening with a local kernel'"'"'s statistics.

5. The method according to claim 1, further comprising:

providing a plurality of target signatures; and

repeating for each target signature of the plurality of target signatures said generating a detection score, said generating a detection map, and said determining from the detection map at least one of a presence and an absence of a target.

6. A method of target detection, wherein physical measurement data is collected from a sensor and converted to radiance data, the radiance data comprising a plurality of radiance data points, each radiance data point of the plurality of radiance data points comprising a multi-dimensional vector, the method comprising:

generating a detection score by processing the radiance data using a discriminant function, the detection score comprising a plurality of detection score points corresponding to the plurality of radiance data points, each detection score point of the plurality of detection score points comprising a target-likelihood value, the discriminant function derived from a linear log likelihood ratio principle;

generating a detection map by applying a threshold to the detection score, the detection map comprising a plurality of detection map points corresponding to the plurality of radiance data points, each detection map point comprising one of a target-indicating value and a clutter-indicating value; and

determining from the detection map one of a presence and an absence of a target.

7. The method according to claim 6, wherein the discriminant function is represented by a curved decision surface separating at least one target from clutter.

8. The method according to claim 6, wherein said generating a detection score by processing the radiance data using a discriminant function comprises whitening the radiance data.

9. The method according to claim 8, wherein said whitening the radiance data comprises one of whitening with global statistics, whitening with local statistics, and whitening with a local kernel'"'"'s statistics.

10. The method according to claim 6, further comprising:

providing a plurality of target signatures; and

repeating for each target signature of the plurality of target signatures said generating a detection score, said generating a detection map, and said determining from the detection map at least one of a presence and an absence of a target.