Systems and methods for adaptive wind noise rejection

1. A method of rejecting wind noise, comprising:

distributing a plurality of acoustic sensors over a surface of a body;

identifying at least one sensor of the plurality of acoustic sensors that is subject to low wind noise to obtain at least one identified sensor;

passing signals from the at least one identified sensor as low wind noise signals; and

rejecting signals from non-identified sensors of the plurality of acoustic sensors as high wind noise signals.

2. The method of claim 1 , wherein identifying at least one sensor of the plurality of acoustic sensors further comprises:

identifying at least one sensor of the plurality of acoustic sensors as a function of a rotation of the body.

3. The method of claim 1 , wherein the plurality of acoustic sensors comprise N sensors and wherein signals from the plurality of acoustic sensors comprise the vector S=[S 1 S 2 . . . S N ] T .

4. The method of claim 3 , wherein identifying the at least one sensor of the plurality of acoustic sensors further comprises:

determining a covariance matrix R of the signals from the N sensors, wherein R=E{S S T } and wherein E is the expected value.

5. The method of claim 4 , wherein identifying the at least one sensor of the plurality of acoustic sensors further comprises:

determining an optimal minimum variance weight vector w, wherein w=[w 1 w 2 . . . w N ] T =R −1 1/1R −1 1 and wherein 1 is a vector of N ones.

6. The method of claim 5 , wherein weight values of weight vector w that correspond to acoustic sensors of the N sensors that are subject to low wind noise are assigned high weights.

7. The method of claim 5 , wherein weight values of weight vector w that correspond to acoustic sensors of the N sensors that are subject to high wind noise are assigned low weights.

8. The method of claim 5 , further comprising:

multiplying the signals from each of the N sensors by corresponding weight values of weight vector w.

9. The method of claim 8 , further comprising:

summing the multiplied signals from each of the plurality of acoustic sensors.

10. The method of claim 1 , wherein passing signals from the at least one identified sensor as low wind noise signals further comprises:

assigning weights having high weight values to signals from the at least one identified sensor.

11. The method of claim 1 , wherein rejecting signals from non-identified sensors of the plurality of acoustic sensors as high wind noise signals further comprises:

assigning weights having low weight values to signals from the non-identified sensors.

12. The method of claim 10 , further comprising:

multiplying the signals from the at least one identified sensor by the assigned weights.

13. The method of claim 12 , further comprising:

summing each of the multiplied signals to produce a noise rejected output signal.

14. The method of claim 1 , wherein the body comprises a three dimensional body.

15. The method of claim 14 , wherein the three dimensional body comprises at least one of a sphere, a cylinder, and a cone.

16. A system for rejecting wind noise incident on a surface of a body, a plurality of acoustic sensors being distributed over the surface of the body, the system comprising:

means for identifying at least one sensor of the plurality of sensors that is subject to a low wind noise;

means for passing signals from the at least one identified sensor as low wind noise signals; and

means for rejecting signals from non-identified sensors of the plurality of sensors as high wind noise signals.

17. A system for rejecting wind noise at a plurality of sensors, comprising:

input logic configured to receive a signal from each of the plurality of sensors;

a processor configured to assign a weight value to each of the received signals; and

output logic configured to derive a wind noise rejected output signal based on a function of the assigned weight values and the received signals.

18. The system of claim 17 , the processor further configured to:

assign a low weight value to a low noise level signal.

19. The system of claim 17 , the processor further configured to:

assign a high weight value to a high noise level signal.

20. The system of claim 17 , wherein the plurality of sensors comprise N sensors and wherein signals from the plurality of acoustic sensors comprise the vector S=[S 1 S 2 . . . S N ] T .

21. The system of claim 20 , the processor further configured to:

determine a covariance matrix R of the signals from the N sensors, wherein R=E{S S T } and wherein E is the expected value.

22. The system of claim 21 , the processor further configured to:

determine an optimal minimum variance weight vector w, wherein w=[w 1 w 2 . . . W N ] T =R −1 1/1R −1 1 and wherein 1 is a vector of N ones.

23. The system of claim 22 , wherein weight values of weight vector w that correspond to sensors of the N sensors that are subject to low wind noise are assigned high weights.

24. The system of claim 22 , wherein weight values of weight vector w that correspond to sensors of the N sensors that are subject to high wind noise are assigned low weights.

25. The system of claim 22 , wherein the output logic comprises multipliers.

26. The system of claim 22 , the multipliers configured to:

multiply the signals from each of the plurality of sensors by corresponding weight values of weight vector w to produce weighted signals.

27. The system of claim 17 , wherein the plurality of sensors comprise pressure sensors.

28. The system of claim 17 , wherein the plurality of sensors sense acoustic and non-acoustic pressure.

29. The system of claim 26 , wherein the output logic further comprises a summer.

30. The system of claim 29 , the summer configured to:

sum the weighted signals to produce the noise rejected output signal.

31. The system of claim 17 , further comprising:

a windscreen comprising a three dimensional self enclosed body, the plurality of sensors being distributed on a surface of the body.

32. A method of rejecting signal noise, comprising:

receiving signals from a plurality of sensors to obtain received signals;

assigning a weight value to each of the received signals; and

deriving a noise rejected output signal based on a function of the assigned weight values and the received signals.

33. The method of claim 32 , further comprising:

assigning a low weight value to a low noise level signal.

34. The method of claim 32 , further comprising:

assigning a high weight value to a high noise level signal.

35. The method of claim 32 , wherein the plurality of sensors comprise N sensors and wherein signals from the plurality of acoustic sensors comprise the vector S=[S 1 S 2 . . . S N ] T .

36. The method of claim 35 , further comprising:

determining a covariance matrix R of the signals from the N sensors, wherein R=E{S S T } and wherein E is the expected value.

37. The method of claim 36 , further comprising:

determining an optimal minimum variance weight vector w, wherein w=[w 1 w 2 . . . w N ] T =R −1 1/1R −1 1 and wherein 1 is a vector of N ones.

38. The method of claim 37 , wherein weight values of weight vector w that correspond to acoustic sensors of the N sensors that are subject to low wind noise are assigned high weights.

39. The method of claim 37 , wherein weight values of weight vector w that correspond to acoustic sensors of the N sensors that are subject to high wind noise are assigned low weights.

40. The method of claim 37 , further comprising:

multiplying the signals from each of the N sensors by corresponding weight values of weight vector w.

41. The method of claim 32 , wherein the plurality of sensors comprise pressure sensors.

42. The method of claim 32 , wherein the plurality of sensors sense acoustic and non-acoustic pressure.

43. The method of claim 40 , further comprising:

summing the weighted signals to produce the noise rejected output signal.

44. The method of claim 32 , further comprising:

distributing the plurality of sensors over a surface of a three dimensional self enclosed body.

45. The method of claim 44 , wherein the body comprises a windscreen.