Method and system for locating a wave source within a defined area

• US 7,969,821 B2
• Filed: 01/17/2007
• Issued: 06/28/2011
• Est. Priority Date: 01/17/2007
• Status: Expired due to Fees

• Current Assignee: Toyota Motor Corporation
• Original Assignee: Toyota Motor Engineering & Manufacturing North America Incorporated (Toyota Motor Corporation)
• Inventors: Yonak, Serdar H.
• Primary Examiner(s): Pihulic; Dan
• Application Number:
  US11/623,920
• Publication Number:
  US 20080170472A1
• Time in Patent Office: 1,623 Days
• Field of Search: 367/125, 367/129, 367/118
• US Class Current: 367/129
• CPC Class Codes: G01S 5/18 using ultrasonic, sonic, or infrasonic waves

Abstract

A method and system for locating a sound source within a defined area in which the sound data is collected with a plurality of spaced apart microphones. The sound data received by the microphones is processed to form a cross spectral density matrix containing vectors of cross correlations and auto correlations of the sound data. A replica vector matrix containing sound data from at least one test sound at a plurality of predetermined locations within the defined area is then constructed. The sound data vectors in the cross spectral density matrix are then projected on the replica vectors in the replica vector matrix to obtain a probability of the location of the sound source at each predetermined location within the defined area. These probabilities form a distribution within the defined area in which the largest probability distribution correlates with the location of the sound source.

Claims

1. A method for locating a sound source within a defined area comprising the steps of:

receiving sound data with a plurality of spaced apart microphones,

creating a cross spectral density matrix containing vectors of cross correlations and auto correlations of the sound data,

creating a replica vector matrix containing sound data from at least one test sound at a plurality of predetermined locations within the defined area,

projecting the sound data vectors on the replica vectors to obtain a probability of the location of the sound source at each predetermined location, said probabilities forming a distribution in the defined area,

outputting a signal representative of the largest probability distribution as the location of the sound source within the defined area.

2. The invention as defined in claim 1 wherein said step of creating the cross spectral density matrix comprises the steps of:

converting the sound data from the time domain to the frequency domain for each microphone to form a frequency domain sound vector data matrix, and

filtering the converted sound data with at least one matched filter corresponding to a predefined sound to form a filtered sound vector data matrix.

3. The invention as defined in claim 2 and comprising the step of disregarding filtered data below a predetermined threshold.

4. The invention as defined in claim 2 wherein said step of creating the cross spectral density matrix further comprises the step of normalizing the filtered sound vector data matrix.

5. The invention as defined in claim 4 wherein said step of creating the cross spectral density matrix further comprises the step of multiplying said normalized filtered sound vector data matrix by its Hermitian transpose.

6. The invention as defined in claim 1 wherein said step of creating the replica vector matrix further comprises the step of calculating the received sound vector data at each microphone from the test sound at each predetermined location in the defined area using a Green'"'"'s function.

7. The invention as defined in claim 6 wherein said step of calculating the received sound vector data at each microphone from the test sound at each predetermined location in the defined area using a Green'"'"'s function further comprises the step of calculating the received sound vector data at each microphone from the test sound at each predetermined location in the defined area using a Green'"'"'s function at a plurality of different frequencies.

8. The invention as defined in claim 1 wherein said projecting step further comprises the step of applying Bartlett'"'"'s processor to said cross spectral density matrix and said replica matrix.

9. The invention as defined in claim 8 and further comprising the step of applying Bartlett'"'"'s processor over a plurality of discrete frequency segments which together form a frequency range, and forming an incoherent average of Bartlett'"'"'s processor for each frequency segment over said frequency range.

10. The invention as defined in claim 1 wherein said projecting step further comprises the step of applying an MV processor to said cross spectral density matrix and said replica matrix.

11. The invention as defined in claim 8 and further comprising the step of applying the MV processor over a plurality of discrete frequency segments which together form a frequency range, and forming an incoherent average of the MV processor for each frequency segment over said frequency range.

12. A system for locating a sound source within a defined area comprising:

at least two microphones which receive sound data with a plurality of spaced apart microphones,

a processor programmed to create a cross spectral density matrix containing vectors of cross correlations and auto correlations of the sound data,

said processor programmed to create a replica vector matrix containing sound data from at least one test sound at a plurality of predetermined locations within the defined area,

said processor programmed to project the sound data vectors on the replica vectors to obtain a probability of the location of the sound source at each predetermined location, said probabilities forming a distribution in the defined area, and

said processor outputting a signal representative of the largest probability distribution as the location of the sound source within the defined area.

13. The system as defined in claim 12 wherein said programmed processor which creates the cross spectral density matrix comprises:

said processor programmed to convert the sound data from the time domain to the frequency domain for each microphone to form a frequency domain sound vector data matrix, and

said processor programmed to filter the converted sound data with at least one matched filter corresponding to a predefined sound to form a filtered sound vector data matrix.

14. The system as defined in claim 13 wherein said processor is programmed to disregard filtered data below a predetermined threshold.

15. The system as defined in claim 13 wherein said processor programmed to create the cross spectral density matrix further comprises said processor programmed to normalize the filtered sound vector data matrix.

16. The system as defined in claim 15 wherein said processor programmed to create the cross spectral density matrix further comprises said processor programmed to multiply said normalized filtered sound vector data matrix by its Hermitian transpose.

17. The invention as defined in claim 12 wherein said processor programmed to create a replica vector matrix further comprises said processor programmed to calculate the received sound vector data at each microphone from the test sound at each predetermined location in the defined area using a Green'"'"'s function.

18. The invention as defined in claim 17 wherein said processor programmed to calculate the received sound vector data at each microphone from the test sound at each predetermined location in the defined area using a Green'"'"'s function further comprises said processor programmed to calculate the received sound vector data at each microphone from the test sound at each predetermined location in the defined area using a Green'"'"'s function at a plurality of different frequencies.

19. The invention as defined in claim 12 wherein said processor programmed to project further comprises said processor programmed to apply Bartlett'"'"'s processor to said cross spectral density matrix and said replica matrix.

20. The invention as defined in claim 19 and said processor programmed to apply Bartlett'"'"'s processor over a plurality of discrete frequency segments which together form a frequency range, and means for forming an incoherent average of Bartlett'"'"'s processor for each frequency segment over said frequency range.

21. The invention as defined in claim 12 wherein said processor programmed to project further comprises said processor programmed to apply an MV processor to said cross spectral density matrix and said replica matrix.

22. The invention as defined in claim 21 and said processor programmed to apply the MV processor over a plurality of discrete frequency segments which together form a frequency range, and said processor programmed to form an incoherent average of the MV processor for each frequency segment over said frequency range.

23. A method for locating a wave source within a defined area comprising the steps of:

receiving wave data with a plurality of spaced apart wave receivers,

creating a cross spectral density matrix containing vectors of cross correlations and auto correlations of the wave data,

creating a replica vector matrix containing wave data from at least one test wave at a plurality of predetermined locations within the defined area,

projecting the wave data vectors on the replica vectors to obtain a probability of the location of the wave source at each predetermined location, said probabilities forming a distribution in the defined area,

outputting a signal representative of the largest probability distribution as the location of the sound source within the defined area.

24. The invention as defined in claim 23 wherein said step of creating the cross spectral density matrix comprises the steps of:

converting the sound data from the time domain to the frequency domain for each wave receiver to form a frequency domain sound vector data matrix, and

filtering the converted sound data with at least one matched filter corresponding to a predefined sound to form a filtered sound vector data matrix.

25. The invention as defined in claim 24 and comprising the step of disregarding filtered data below a predetermined threshold.

26. The invention as defined in claim 24 wherein said step of creating the cross spectral density matrix further comprises the step of normalizing the filtered sound vector data matrix.

27. The invention as defined in claim 26 wherein said step of creating the cross spectral density matrix further comprises the step of multiplying said normalized filtered sound vector data matrix by its Hermitian transpose.

28. The invention as defined in claim 23 wherein said step of creating a replica vector matrix further comprises the step of calculating the received sound vector data at each microphone from the test sound at each predetermined location in the defined area using a Green'"'"'s function.

29. The invention as defined in claim 28 wherein said step of calculating the received sound vector data at each microphone from the test sound at each predetermined location in the defined area using a Green'"'"'s function further comprises the step of calculating the received sound vector data at each microphone from the test sound at each predetermined location in the defined area using a Green'"'"'s function at a plurality of different frequencies.

30. The invention as defined in claim 23 wherein said projecting step further comprises the step of applying Bartlett'"'"'s processor to said cross spectral density matrix and said replica matrix.

31. The invention as defined in claim 30 and further comprising the step of applying Bartlett'"'"'s processor over a plurality of discrete frequency segments which together form a frequency range, and forming an incoherent average of Bartlett'"'"'s processor for each frequency segment over said frequency range.

32. The invention as defined in claim 23 wherein said wave source comprises a sound wave source.

33. The invention as defined in claim 23 wherein said projecting step further comprises the step of applying an MV processor to said cross spectral density matrix and said replica matrix.

34. The invention as defined in claim 33 and further comprising the step of applying the MV processor over a plurality of discrete frequency segments which together form a frequency range, and forming an incoherent average of the MV processor for each frequency segment over said frequency range.