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2020 Vol.39, Issue 3 Preview Page

Research Article

Analysis on performance of grid-free compressive beamforming based on experiment

실험 기반 무격자 압축 빔형성 성능 분석

Myoungin Shin1
,
Youngbin Cho2
,
Youngmin Choo1
,
Keunhwa Lee1
,
Jungpyo Hong3
,
Seongil Kim3
,
Wooyoung Hong1∗
신 명인1
,
조 영빈2
,
추 영민1
,
이 근화1
,
홍 정표3
,
김 성일3
,
홍 우영1∗
1세종대학교 국방시스템공학과
2상지이앤씨(주)
3국방과학연구소 해양과학기술원
∗Corresponding Author
31 May 2020. pp. 179-190
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Abstract

In this paper, we estimated the Direction of Arrival (DOA) using Conventional BeamForming (CBF), adaptive beamforming and compressive beamforming. Minimum Variance Distortionless Response (MVDR) and Multiple Signal Classification (MUSIC) are used as the adaptive beamforming, and grid-free compressive sensing is applied for the compressive sensing beamforming. Theoretical background and limitations of each technique are introduced, and the performance of each technique is compared through simulation and real experiments. The real experiments are conducted in the presence of reflected signal, transmitting a sound using two speakers and receiving acoustic data through a linear array consisting of eight microphones. Simulation and experimental results show that the adaptive beamforming and the grid-free compressive beamforming have a higher resolution than conventional beamforming when there are uncorrelated signals. On the other hand, the performance of the adaptive beamforming is degraded by the reflected signals whereas the grid-free compressive beamforming still improves the conventional beamforming resolution regardless of reflected signal presence.

Keywords
Conventional beamforming
Adaptive beamforming
Compressive beamforming
Direction of Arrival (DOA) estimation

본 논문에서는 일반 빔형성, 적응 빔형성, 압축센싱을 이용하여 도래각을 추정한다. 이때, 적응 빔형성으로 Minimum Variance Distortionless Response(MVDR)과 Multiple Signal Classification(MUSIC)을 사용하였고, 압축 빔형성(compressive sensing beamforming)을 위해 무격자 압축센싱(grid-free compressive sensing)을 적용하였다. 앞서 언급한 각 기법들의 이론적 배경 및 한계점을 소개하고 모의실험 및 실제 실험을 통해 각 기법의 성능을 비교하였다. 실제 실험은 반사 신호가 존재하는 환경에서 수행하였으며, 2대의 스피커를 통해 음원을 송신하고 8대의 마이크로폰을 사용한 선배열을 통해 데이터를 수신하였다. 모의실험 및 실제 실험 결과를 통해 상관신호가 존재하지 않을 때, 적응 빔형성과 무격자 압축 빔형성이 일반 빔형성보다 고해상도의 결과를 보임을 확인하였다. 반면, 반사 신호가 존재하는 환경에서 적응 빔형성의 성능이 저하되지만, 무격자 압축 빔형성의 경우 반사 신호의 영향을 받지 않고 일반 빔형성의 분해능이 개선됨을 확인하였다.

키워드
일반 빔형성
적응 빔형성
압축 빔형성
도래각 추정
References
1
F. B. Jensen, W. A. Kuperman, M. B. Porter, and H. Schmidt, Computational Ocean Acoustics (Springer, New York, 2011), Chap. 10.
10.1007/978-1-4419-8678-8
2
D. H. Johnson, "The application of spectral estimation methods to bearing estimation problems," Proc. IEEE, 70, 1018-1028 (1982).
10.1109/PROC.1982.12430
3
S. A. Vorobyov, "Principles of minimum variance robust adaptive beamforming design," Signal Processing, 93, 3264-3277 (2013).
10.1016/j.sigpro.2012.10.021
4
R. O. Schmidt, "Multiple emitter location and signal parameter estimation," IEEE. Trans. Antennas Propagation. 34, 276-280 (1986).
10.1109/TAP.1986.1143830
5
I. S. Reed, J. D. Mallett, and L. E. Brennen, "Rapid convergence rate in adaptive arrays," IEEE. Trans. On AES-10, 853-863 (1974).
10.1109/TAES.1974.307893
6
T. Shan and T. Kailath, "Adaptive beamforming for coherent signals and interference," IEEE. Trans. On ASSP. 33, 527-536 (1985).
10.1109/TASSP.1985.1164583
7
S. U. Pillai and B. H. Kwon, "Forward/backward spatial smoothing techniques for coherent signal identification," IEEE. Trans. Acoust. 37, 8-15 (1989)
10.1109/29.17496
8
B. D. Carlson, "Covariance matrix estimation errors and diagonal loading in adaptive arrays," IEEE Trans. Aero. Electron. Sys. 24, 397-401 (1988).
10.1109/7.7181
9
D. L. Donoho, "Compressed sensing," IEEE Trans. Inf. Theory, 52, 1289-1306 (2006).
10.1109/TIT.2006.871582
10
M. Lustig, D. Donoho, and J. M. Pauly, "Sparse MRI: The application of compressed sensing for rapid MR imaging," Magn. Reson. Med. 58, 1182-1195 (2007).
10.1002/mrm.2139117969013
11
M. A. Herman and T. Strohmer, "High-resolution radar via compressed sensing," IEEE Trans. Signal Process. 57, 2275-2284 (2009).
10.1109/TSP.2009.2014277
12
A. Xenaki, P. Gerstoft, and K. Mosegaard, "Compressive beamforming," J. Acoust. Soc. Am. 136, 260-271 (2014).
10.1121/1.488336024993212
13
K. L. Gemba, W. S. Hodgkiss, and P. Gerstoft, "Adaptive and compressive matched field processing," J. Acoust. Soc. Am. 141 (2017).
10.1121/1.497352828147570
14
P. Gerstoft, C. F. Mechlenbrauker, W. Seong, and M. Bianco, "Introdunction to compressive sensing in acoustics," J. Acoust. Soc. Am. 143, 3731 (2018).
10.1121/1.504308929960475
15
M. Bianco and P. Gerstoft, "Compressive acoustic sound speed profile estimation," J. Acoust. Soc. Am. 139, EL90-EL94 (2016).
10.1121/1.494378427036293
16
P. Gerstoft, "Multiple and single snapshot compressive beamforming," J. Acoust. Soc. Am. 138, 2003 (2015).
10.1121/1.492994126520284
17
C. Yardim, P. Gerstoft, W. Hodgkiss, and J. Traer, "Compressive geoacoustic inversion," J. Acoust. Soc. Am. 135, 1245-1255 (2014).
10.1121/1.486479224606266
18
Y. Park, W. Seong, and Y. Choo, "Compressive time delay estimation off the grid," J. Acoust. Soc. Am. 141, EL585-EL591 (2017).
10.1121/1.498561228679237
19
Y. Choo and W. Seong, "Compressive sound speed profile inversion using beamforming results," Remote. Sens. 10, 704 (2018).
10.3390/rs10050704
20
E. J. Candles, J. Romberg, and T. Tao, "Robust uncertainty princiles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Inf. Theory, 52, 489-509 (2006).
10.1109/TIT.2005.862083
21
E. J. Candles and T. Tao, "Near-optimal signal recovery from random projections: Universal encoding strategies," IEEE Trans. Inf. Theory, 52, 5406-5425 (2006).
10.1109/TIT.2006.885507
22
V. Chandrasekaran, B. Recht, P. A. Parrilo, and A. S. Willsky, "The convex geometry of linear inverse problems," Found. Comput. Math. 12, 805-849 (2012).
10.1007/s10208-012-9135-7
23
E. J. Candès and C. Fernandez-Granda, "Towards a mathematical theory of super-resolution," Comm. Pure. Appl. Math, 67, 906-956 (2012).
10.1002/cpa.21455
24
A. Xenaki and P. Gerstoft, "Grid-free compressive beamforming," J. Acoust. Soc. Am. 137, 1923-1935 (2015).
10.1121/1.491626925920844
25
Y. Choo, J. Park, Y. Park, M. Park, and W. Seong, "Grid-free channel estimation of acoustic measurement data from SAVEX15," J. Acoust. Soc. Am. 140, 3055 (2016).
10.1121/1.4969496
26
Y. Park, Y. Choo, and W. Seong, "Off-the-grid direction- of-arrival estimation with multiple measurement vectors," J. Acoust. Soc. Am. 142, 2728-2728 (2018).
10.1121/1.5014960
27
G. Tang, B. N. Bhaskar, P. Shah, and B. Recht, "Compressed sensing off the grid," IEEE Trans. Inf. Theory, 59, 7465-7490 (2013).
10.1109/TIT.2013.2277451
28
S. Boyd and L. Vandenberghe, Convex Optimization (Cambridge Univ. Press, New York, 2004), pp.215- 231.
10.1017/CBO9780511804441
29
CVX: Matlab Software for Disciplined Convex Programming, http://cvxr.com/cvx/, (Last viewed March 5, 2020).
30
Y. Cho, Analysis on performance of various beamforming schemes using experimental data (in Korean), (Master thesis, Sejong University, 2019).
Information
  • Publisher :The Acoustical Society of Korea
  • Publisher(Ko) :한국음향학회
  • Journal Title :The Journal of the Acoustical Society of Korea
  • Journal Title(Ko) :한국음향학회지
  • Volume : 39
  • No :3
  • Pages :179-190
  • Received Date : 2020-03-06
  • Accepted Date : 2020-05-07
  • DOI :https://doi.org/10.7776/ASK.2020.39.3.179
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