Method for building robust algorithms that classify objects using high-resolution radar signals

• US 20070253625A1
• Filed: 04/28/2006
• Published: 11/01/2007
• Est. Priority Date: 04/28/2006
• Status: Abandoned Application

• Current Assignee: BBN Technologies Corporation (Rtx Corporation)
• Original Assignee: BBNT Solutions LLC (Rtx Corporation)
• Inventors: Yi, Gina
• Application Number:
  US11/413,508
• Publication Number:
  US 20070253625A1
• Time in Patent Office: Days
• US Class Current: 382/228
• CPC Class Codes: G01S 13/89 for mapping or imaging
• CPC Class Codes: G01S 7/412 based on a comparison between measured values and known or stored values
• CPC Class Codes: G06F 18/211 Selection of the most significant subset of features

Abstract

A system and method are provided for classifying objects using high resolution radar signals. The method includes determining a probabilistic classifier of an object from a high resolution radar scan, determining a deterministic classifier of the object from the high resolution radar scan, and classifying the object based on the probabilistic classifier and the deterministic classifier.

Claims

1. A method for classifying objects using high resolution radar signals, comprising:

determining a probabilistic classifier of an object from a high resolution radar scan;

determining a deterministic classifier of the object from the high resolution radar scan; and

classifying the object based on the probabilistic classifier and the deterministic classifier.

2. The method of claim 1, wherein the step of determining the probabilistic classifier includes:

selecting a feature-set consisting of features extracted from the high resolution radar scan;

selecting a probability density function (PDF) and corresponding parameter-values for each feature extracted from the high resolution radar scan; and

assembling the probabilistic classifier using the selected feature-set and the selected PDFs and their corresponding parameter-values.

3. The method of claim 2, wherein the corresponding parameters include an angular range for the extracted feature-values.

4. The method of claim 2, wherein the extracted feature-values from the high resolution radar scan correspond to a known classification class from a training data set and a known set of probabilistic classification features from the training data set.

5. The method of claim 2, wherein selecting the PDF and the corresponding parameter-values includes modeling a statistical distribution of each feature with a plurality of parametric PDFs.

6. The method of claim 5, further comprising:

estimating the corresponding parameter-values using Maximum Likelihood Parameter Estimation; and

computing a statistic ‘

Q’

of the Chi-Squared Test of Goodness-of-Fit for seach parametric PDF.

7. The method of claim 6, wherein the parametric PDF with the lowest value of ‘

Q’ and

its corresponding parameter-values are selected.

8. The method of claim 2, wherein selecting the feature-set consisting of features extracted from the high resolution radar scan includes:

computing a probabilistic likelihood value from the extracted feature-values for each class using its joint PDF; and

classifying the extracted feature-values by selecting the class that produces the highest likelihood value.

9. The method of claim 8, further comprising determining the classification accuracy rate from the likelihood values.

10. The method of claim 2, wherein assembling the probabilistic classifier includes:

computing a probabilistic likelihood value from a joint PDF of each class; and

selecting the PDF that produces the highest likelihood value.

11. The method of claim 10, wherein the step of computing a probabilistic likelihood value further includes using an angular range for the extracted feature-values.

12. The method of claim 10, further comprising assigning a level of confidence to the selected PDF.

13. The method of claim 12, wherein the level of confidence is determined by an average of classification accuracy rates.

14. The method of claim 1, wherein determining the deterministic classifier of the object includes:

selecting a feature-set consisting of features extracted from the high resolution radar scan; and

assembling the deterministic classifier using the selected feature-set.

15. The method of claim 14, wherein selecting the features-set consisting of features extracted from the high resolution radar scan includes:

averaging the extracted feature-values; and

classifying the averaged value.

16. The method of claim 14, wherein assembling the deterministic classifier includes classifying the averaged value.

17. The method of claim 16, further comprising assigning a level of confidence to the classification decision.

18. The method of claim 1, wherein classifying the object includes outputting a classification type to a user.

19. The method of claim 18, wherein the classification types include a set of objects and unknown.

20. The method of claim 19, wherein the set of objects include a human and a vehicle.

21. The method of claim 18, wherein outputting the classification type is determined by assessing outputs of the probabilistic classifier and outputs of the deterministic classifier.

22. The method of claim 21, wherein the deterministic classifier takes precedence over the probabilistic classifier.

23. The method of claim 1, wherein the high resolution radar scan includes bistatic signals or multistatic signals.

24. The method of claim 1, wherein the high resolution radar scan includes a plurality of high resolution radar scans.

25. A system for classifying objects using high resolution radar signals, comprising:

a high resolution radar signal module for producing a high resolution radar scan;

a probabilistic classifier module for determining an object from the high resolution radar scan;

a deterministic classifier module for determining the object from the high resolution radar scan; and

an object classification module for classifying the object based on the probabilistic classifier and the deterministic classifier.

26. The system of claim 25, wherein the probabilistic classifier module includes:

a feature-set module for selecting a feature-set consisting of features extracted from the high resolution radar scan;

a probability density finction (PDF) module for selecting a PDF and corresponding parameter-values for each feature extracted from the high resolution radar scan; and

an assembly module for assembling the probabilistic classifier using the selected feature-set and the selected PDFs and their corresponding parameter-values.

27. The system of claim 26, wherein the corresponding parameters include an angular range for the extracted feature-values.

28. The system of claim 26, wherein the extracted feature-values from the high resolution radar scan correspond to a known classification class from a training data set and a known set of probabilistic classification features from the training data set.

29. The system of claim 26, wherein the PDF module models a statistical distribution of each feature with a plurality of parametric PDFs.

30. The system of claim 29, further comprising:

an estimation module for estimating the corresponding parameter-values using Maximum Likelihood Parameter Estimation; and

a computation module for computing a statistic ‘

Q’

of the Chi-Squared Test of Goodness-of-Fit for each parametric PDF.

31. The system of claim 30, wherein the parametric PDF with the lowest value of ‘

Q’ and

its corresponding parameter-values are selected.

32. The system of claim 26, wherein the feature-set module:

a likelihood module for computing a probabilistic likelihood value from the extracted feature-values for each class using its joint PDF; and

a classifying module for classifying the extracted feature-values by selecting the class that produces the highest likelihood value.

33. The system of claim 32, further comprising a determination module for determining the classification accuracy rate from the likelihood values.

34. The system of claim 26, wherein the assembly module:

a likelihood value module for computing a probabilistic likelihood value from a joint PDF of each class; and

a PDF selection module for selecting the PDF that produces the highest likelihood value.

35. The system of claim 34, wherein the likelihood value module further includes using an angular range for the extracted feature-values.

36. The system of claim 34, further comprising a confidence module for assigning a level of confidence to the selected PDF.

37. The system of claim 36, wherein the level of confidence is determined by an average of classification accuracy rates.

38. The system of claim 25, wherein the deterministic classifier module includes:

a feature-set selection module for selecting a feature-set consisting of features extracted from the high resolution radar scan; and

a deterministic classifier assembly module for assembling the deterministic classifier using the selected feature-set.

39. The system of claim 38, wherein the features-set selection module includes:

an averaging module for averaging the extracted feature-values; and

a classification module for classifying the averaged value.

40. The system of claim 38, wherein the deterministic classifier assembly module includes classifying the averaged value.

41. The system of claim 40, further comprising a deterministic confidence module for assigning a level of confidence to the classification decision.

42. The system of claim 25, wherein the object classification module includes an output module for outputting a classification type to a user.

43. The system of claim 42, wherein the classification types include a set of objects and unknown.

44. The system of claim 43, wherein the set of objects include a human and a vehicle.

45. The system of claim 42, wherein outputting the classification type is determined by assessing outputs of the probabilistic classifier and outputs of the deterministic classifier.

46. The system of claim 45, wherein the deterministic classifier takes precedence over the probabilistic classifier.

47. The system of claim 25, wherein the high resolution radar scan includes bistatic signals or multistatic signals.

48. The system of claim 25, wherein the high resolution radar scan includes a plurality of high resolution radar scans.

49. A computer readable medium whose contents cause a computer system to classifying objects using high resolution radar signals, the computer system performing the steps of:

determining a probabilistic classifier of an object from a high resolution radar scan;

determining a deterministic classifier of the object from the high resolution radar scan; and

classifying the object based on the probabilistic classifier and the deterministic classifier.

50. A method for classifying objects using high resolution radar signals, comprising:

means for determining a probabilistic classifier of an object from a high resolution radar scan;

means for determining a deterministic classifier of the object from the high resolution radar scan; and

means for classifying the object based on the probabilistic classifier and the deterministic classifier.