1887
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
oa
Compressive sensing beamforming based on covariance for acoustic imaging with noisy measurements
Rent:
Rent this article for
Access full text Article
/content/asa/journal/jasa/134/5/10.1121/1.4824630
1.
1. E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52(2), 489509 (2006).
http://dx.doi.org/10.1109/TIT.2005.862083
2.
2. E. J. Candes, “Near-optimal signal recovery from random projections: universal encoding strategies?,” IEEE Trans. Inf. Theory 52(12), 54065425 (2006).
http://dx.doi.org/10.1109/TIT.2006.885507
3.
3. E. J. Candes and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag. 25(2), 2130 (2008).
http://dx.doi.org/10.1109/MSP.2007.914731
4.
4. J. Romberg, “Imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 1420 (2008).
http://dx.doi.org/10.1109/MSP.2007.914729
5.
5. R. G. Baraniuk, “More is less: signal processing and the data deluge,” Science 331(11), 717719 (2011).
http://dx.doi.org/10.1126/science.1197448
6.
6. B. D. Van Veen and K. M. Buckley, “Beamforming: A versatile approach to spatial filtering,” IEEE ASSP Mag. 5(2), 424 (1988).
http://dx.doi.org/10.1109/53.665
7.
7. R. A. Gramann and J. W. Mocio, “Aeroacoustic measurements in wind tunnels using adaptive beamforming methods,” J. Acoust. Soc. Am. 97(6), 36943701 (1995).
http://dx.doi.org/10.1121/1.412386
8.
8. Y. T. Cho and M. J. Roan, “Adaptive near-field beamforming techniques for sound source imaging,” J. Acoust. Soc. Am. 125(2), 944957 (2009).
http://dx.doi.org/10.1121/1.3050248
9.
9. T. Yardibi, J. Li, P. Stoica, and L. N. Cattafesta, “Sparsity constrained deconvolution approaches for acoustic source mapping,” J. Acoust. Soc. Am. 123(5), 26312642 (2008).
http://dx.doi.org/10.1121/1.2896754
10.
10. Y. Liu, A. R. Quayle, A. P. Dowling, and P. Sijtsma, “Beamforming correction for dipole measurement using two-dimensional microphone arrays,” J. Acoust. Soc. Am. 124(1), 182191 (2008).
http://dx.doi.org/10.1121/1.2931950
11.
11. L. Bai and X. Huang, “Observer-based beamforming algorithm for acoustic array signal processing,” J. Acoust. Soc. Am. 130(6), 38033811 (2011).
http://dx.doi.org/10.1121/1.3658448
12.
12. X. Huang, L. Bai, I. Vinogradov, and E. Peers, “Adaptive beamforming for array signal processing in aeroacoustic measurements,” J. Acoust. Soc. Am. 131(3), 21522161 (2012).
http://dx.doi.org/10.1121/1.3682041
13.
13. A. C. Gurbuz, J. H. McClellan, and V. Cevher, “A compressive beamforming method,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2008 (2008).
14.
14. G. F. Edelmann and C. F. Gaumond, “Beamforming using compressive sensing,” J. Acoust. Soc. Am. 130(4), EL232EL237 (2011).
15.
15. D. Malioutov, M. Cetin, and A. S. Willsky, “A sparse signal reconstruction perspective for source localization with sensor arrays,” IEEE Trans. Sig. Proc. 53(8), 30103022 (2005).
http://dx.doi.org/10.1109/TSP.2005.850882
16.
16. N. Wagner, Y. C. Eldar, and Z. Friedman, “Compressed beamforming in ultrasound imaging,” IEEE Trans. Sig. Proc. 60(9), 46434657 (2012).
http://dx.doi.org/10.1109/TSP.2012.2200891
17.
17. S. Boyd and L. Vandenberghe, Convex Optimization (Cambridge University Press, New York, 2004).
18.
18. A. C. Gurbuz, V. Cevher, and J. H. McClellan, “Bearing estimation via spatial sparsity using compressive sensing,” IEEE Trans. Aerosp. Electron. Syst. 48(2), 13581369 (2012).
http://dx.doi.org/10.1109/TAES.2012.6178067
19.
19. X. Huang, X. Zhang, and Y. Li, “Broadband flow-induced sound control using plasma actuators,” J. Sound Vib. 329(13), 24772489 (2010).
http://dx.doi.org/10.1016/j.jsv.2010.01.018
20.
20. T. J. E. Mueller, Aeroacoustic Measurements (Springer, Germany, 2002).
http://aip.metastore.ingenta.com/content/asa/journal/jasa/134/5/10.1121/1.4824630
Loading
/content/asa/journal/jasa/134/5/10.1121/1.4824630
Loading

Data & Media loading...

Loading

Article metrics loading...

/content/asa/journal/jasa/134/5/10.1121/1.4824630
2013-10-16
2014-09-23

Abstract

Compressive sensing, a newly emerging method from information technology, is applied to array beamforming and associated acoustic applications. A compressive sensing beamforming method (CSB-II) is developed based on sampling covariance matrix, assuming spatially sparse and incoherent signals, and then examined using both simulations and aeroacoustic measurements. The simulation results clearly show that the proposed CSB-II method is robust to sensing noise. In addition, aeroacoustic tests of a landing gear model demonstrate the good performance in terms of resolution and sidelobe rejection.

Loading

Full text loading...

/deliver/fulltext/asa/journal/jasa/134/5/1.4824630.html;jsessionid=35atajru8efct.x-aip-live-06?itemId=/content/asa/journal/jasa/134/5/10.1121/1.4824630&mimeType=html&fmt=ahah&containerItemId=content/asa/journal/jasa

Most read this month

Article
content/asa/journal/jasa
Journal
5
3
Loading

Most cited this month

true
true
This is a required field
Please enter a valid email address
This feature is disabled while Scitation upgrades its access control system.
This feature is disabled while Scitation upgrades its access control system.
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Compressive sensing beamforming based on covariance for acoustic imaging with noisy measurements
http://aip.metastore.ingenta.com/content/asa/journal/jasa/134/5/10.1121/1.4824630
10.1121/1.4824630
SEARCH_EXPAND_ITEM