Advertisement

Comparison of Deconvolution Algorithms of Phased Microphone Array for Sound Source Localization in an Airframe Noise Test

Conference paper
  • 393 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 622)

Abstract

Nowadays, Phased microphone arrays have a powerful capability for acoustic source localization. The conventional beamforming constructs a dirty map of source distributions from array microphone pressure signals. Compared with conventional beamforming, deconvolution algorithms, such as DAMAS, CLEAN-SC, NNLS, FISTA and SpaRSA, can significantly improve the spatial resolution but require high computational effort. The performances of these deconvolution algorithms have been compared using simulated applications and experimental applications with simple sound source distributions. However, these comparisons are not carried out in experimental applications with complex sound source distributions. In this paper, the performances of five deconvolution algorithms (DAMAS, CLEAN-SC, NNLS, FISTA and SpaRSA) are compared in an airframe noise test, which contains very complex sound source distributions. DAMAS and CLEAN-SC achieve better spatial resolution than NNLS, FISTA and SpaRSA. DAMAS need more computational effort compared with CLEAN-SC. In addition, DAMAS can significantly reduce computational run time using compression computational grid. DAMAS with compression computational grid and CLEAN-SC are thus recommended for source localizations in experimental applications with complex sound distributions.

Keywords

Microphone array Beamforming Deconvolution algorithms Airframe noise 

Notes

Acknowledgements

The authors would like to thank Dr. Thomas Geyer of BTU Cottbus-Senftenberg, Germany for providing the login information of the DLR1 benchmark test. This work was supported by China Scholarship Council and the Natural Science Foundation of China (Grant NO. 51506121).

References

  1. 1.
    Michel U (2006) History of acoustic beamforming. In: Proceedings of 1st Berlin beamforming conference2006Google Scholar
  2. 2.
    Frieden BR (1972) Restoring with maximum likelihood and maximum entropy. J Opt Soc Am 62(4):511–518ADSCrossRefGoogle Scholar
  3. 3.
    Banham MR, Katsaggelos AK (1977) Digital image restoration. IEEE Signal Process Mag 14(2):24–41CrossRefGoogle Scholar
  4. 4.
    Gull SF, Daniell GJ (1978) Image reconstruction from incomplete and noisy data. Nature 272(5655):686–690ADSCrossRefGoogle Scholar
  5. 5.
    Narayan R, Nityananda R (1986) Maximum entropy image restoration in astronomy. Ann Rev Astron Astrophys 24(1):127–170ADSCrossRefGoogle Scholar
  6. 6.
    Lawson CL, Hanson RJ (1995) Solving least squares problems. Math Comput 30(135):665zbMATHGoogle Scholar
  7. 7.
    Dougherty RP, Stoker RW (1998) Sidelobe suppression for phased array aeroacoustic measurements. In: 4th AIAA/CEAS aeroacoustics conferenceGoogle Scholar
  8. 8.
    Sijtsma P (2009) CLEAN based on spatial source coherence. Int J Aeroacoustics 6(4):357–374CrossRefGoogle Scholar
  9. 9.
    Sarradj E, Herold G, Sijtsma P, Merino Martinez R, Geyer TF, Bahr CJ, Porteous R, Moreau D, Doolan CJ (2017) A microphone array method benchmarking exercise using synthesized input data. In: 23rd AIAA/CEAS aeroacoustics conferenceGoogle Scholar
  10. 10.
    Brooks TF, Humphreys WM (2004) A deconvolution approach for the mapping of acoustic sources (DAMAS) determined from phased microphone arrays. In: 10th AIAA/CEAS aeroacoustics conferenceGoogle Scholar
  11. 11.
    Brooks TF, Humphreys WM (2006) A deconvolution approach for the mapping of acoustic sources ( DAMAS) determined from phased microphone arrays. J Sound Vib 294(4):856–879ADSCrossRefGoogle Scholar
  12. 12.
    Brooks TF, Humphreys WM (2005) Three-dimensional applications of DAMAS methodology for aeroacoustic noise source definition. In: 11th AIAA/CEAS aeroacoustics conferenceGoogle Scholar
  13. 13.
    Brooks TF, Humphreys WM (2006) Extension of DAMAS phased array processing for spatial coherence determination (DAMAS-C). In: 12th AIAA/CEAS aeroacoustics conferencesGoogle Scholar
  14. 14.
    Ma W, Liu X (2017) DAMAS with compression computational grid for acoustic source mapping. J Sound Vib 410:473–484ADSCrossRefGoogle Scholar
  15. 15.
    Ma W, Liu X (2017) Improving the efficiency of DAMAS for sound source localization via wavelet compression computational grid. J Sound Vib 395:341–353CrossRefGoogle Scholar
  16. 16.
    Ma W, Liu X (2018) Compression computational grid based on functional beamforming for acoustic source localization. Appl Acoust 134:75–87CrossRefGoogle Scholar
  17. 17.
    Ehrenfried K, Koop L (2007) Comparison of iterative deconvolution algorithms for the mapping of acoustic sources. AIAA J 45(7):1–19CrossRefGoogle Scholar
  18. 18.
    Dougherty RP (2013) Extensions of DAMAS and benefits and limitations of deconvolution in beamforming. In: 11th AIAA/CEAS aeroacoustics conferenceGoogle Scholar
  19. 19.
    Lucy LB (1974) An iterative technique for the rectification of observed distributions. Astron J 79(6):745–754ADSCrossRefGoogle Scholar
  20. 20.
    Richardson WH (1972) Bayesian-based iterative method of image restoration. J Opt Soc Am 62(1):55–59ADSMathSciNetCrossRefGoogle Scholar
  21. 21.
    Herold G, Geyer TF, Sarradj E (2017) Comparison of inverse deconvolution algorithms for high-resolution aeroacoustic source characterization. In: 23rd AIAA/CEAS aeroacoustics conferenceGoogle Scholar
  22. 22.
    Bahr CJ, Humphreys WM, Ernst D, Ahlefeldt T, Spehr C, Pereira A, Leclre Q, Picard C, Porteous R, Moreau D, Fischer JR, Doolan CJ (2017) A comparison of microphone phased array methods applied to the study of airframe noise in wind tunnel testing. In: 23rd AIAA/CEAS aeroacoustics conferenceGoogle Scholar
  23. 23.
    Ahlefeldt T (2013) Aeroacoustic measurements of a scaled half-model at high reynolds numbers. AIAA J 51(12):2783–2791ADSCrossRefGoogle Scholar
  24. 24.
    Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2(1):183–202MathSciNetCrossRefGoogle Scholar
  25. 25.
    Wright SJ, Nowak RD, Figueiredo MAT (2009) Sparse reconstruction by separable approximation. IEEE Trans Signal Process 57(7):2479–2493ADSMathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Aeronautics and AstronauticsShanghai Jiao Tong UniversityShanghaiPeople’s Republic of China

Personalised recommendations