Skip to main content

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

  • Conference paper
  • First Online:
Proceedings of the International Conference on Aerospace System Science and Engineering 2019 (ICASSE 2019)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 622))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Michel U (2006) History of acoustic beamforming. In: Proceedings of 1st Berlin beamforming conference2006

    Google Scholar 

  2. Frieden BR (1972) Restoring with maximum likelihood and maximum entropy. J Opt Soc Am 62(4):511–518

    Article  ADS  Google Scholar 

  3. Banham MR, Katsaggelos AK (1977) Digital image restoration. IEEE Signal Process Mag 14(2):24–41

    Article  Google Scholar 

  4. Gull SF, Daniell GJ (1978) Image reconstruction from incomplete and noisy data. Nature 272(5655):686–690

    Article  ADS  Google Scholar 

  5. Narayan R, Nityananda R (1986) Maximum entropy image restoration in astronomy. Ann Rev Astron Astrophys 24(1):127–170

    Article  ADS  Google Scholar 

  6. Lawson CL, Hanson RJ (1995) Solving least squares problems. Math Comput 30(135):665

    MATH  Google Scholar 

  7. Dougherty RP, Stoker RW (1998) Sidelobe suppression for phased array aeroacoustic measurements. In: 4th AIAA/CEAS aeroacoustics conference

    Google Scholar 

  8. Sijtsma P (2009) CLEAN based on spatial source coherence. Int J Aeroacoustics 6(4):357–374

    Article  Google Scholar 

  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 conference

    Google Scholar 

  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 conference

    Google Scholar 

  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):856–879

    Article  ADS  Google Scholar 

  12. Brooks TF, Humphreys WM (2005) Three-dimensional applications of DAMAS methodology for aeroacoustic noise source definition. In: 11th AIAA/CEAS aeroacoustics conference

    Google Scholar 

  13. Brooks TF, Humphreys WM (2006) Extension of DAMAS phased array processing for spatial coherence determination (DAMAS-C). In: 12th AIAA/CEAS aeroacoustics conferences

    Google Scholar 

  14. Ma W, Liu X (2017) DAMAS with compression computational grid for acoustic source mapping. J Sound Vib 410:473–484

    Article  ADS  Google Scholar 

  15. Ma W, Liu X (2017) Improving the efficiency of DAMAS for sound source localization via wavelet compression computational grid. J Sound Vib 395:341–353

    Article  Google Scholar 

  16. Ma W, Liu X (2018) Compression computational grid based on functional beamforming for acoustic source localization. Appl Acoust 134:75–87

    Article  Google Scholar 

  17. Ehrenfried K, Koop L (2007) Comparison of iterative deconvolution algorithms for the mapping of acoustic sources. AIAA J 45(7):1–19

    Article  Google Scholar 

  18. Dougherty RP (2013) Extensions of DAMAS and benefits and limitations of deconvolution in beamforming. In: 11th AIAA/CEAS aeroacoustics conference

    Google Scholar 

  19. Lucy LB (1974) An iterative technique for the rectification of observed distributions. Astron J 79(6):745–754

    Article  ADS  Google Scholar 

  20. Richardson WH (1972) Bayesian-based iterative method of image restoration. J Opt Soc Am 62(1):55–59

    Article  ADS  MathSciNet  Google Scholar 

  21. Herold G, Geyer TF, Sarradj E (2017) Comparison of inverse deconvolution algorithms for high-resolution aeroacoustic source characterization. In: 23rd AIAA/CEAS aeroacoustics conference

    Google Scholar 

  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 conference

    Google Scholar 

  23. Ahlefeldt T (2013) Aeroacoustic measurements of a scaled half-model at high reynolds numbers. AIAA J 51(12):2783–2791

    Article  ADS  Google Scholar 

  24. Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2(1):183–202

    Article  MathSciNet  Google Scholar 

  25. Wright SJ, Nowak RD, Figueiredo MAT (2009) Sparse reconstruction by separable approximation. IEEE Trans Signal Process 57(7):2479–2493

    Article  ADS  MathSciNet  Google Scholar 

Download references

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).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wei Ma .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, J., Ma, W. (2020). Comparison of Deconvolution Algorithms of Phased Microphone Array for Sound Source Localization in an Airframe Noise Test. In: Jing, Z. (eds) Proceedings of the International Conference on Aerospace System Science and Engineering 2019. ICASSE 2019. Lecture Notes in Electrical Engineering, vol 622. Springer, Singapore. https://doi.org/10.1007/978-981-15-1773-0_7

Download citation

Publish with us

Policies and ethics