EI / SCOPUS / CSCD 收录
中文核心期刊
EI / SCOPUS / CSCD 收录
中文核心期刊
| Citation: |
HUANG Jianli, WANG Yu, GONG Zaixiao, WANG Jun, WANG Haibin. Beam-domain deconvolution beamforming algorithm enhanced by compressive sensing[J]. ACTA ACUSTICA, 2025, 50(1): 97-108. DOI: 10.12395/0371-0025.2023135
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Deconvolution beamforming can effectively suppress sidelobes and improve the azimuth resolution of multiple targets. However, traditional deconvolution beamforming methods mostly require the beam pattern to be shift-invariant, so they are only suitable for specific arrays such as linear arrays and circular arrays. Traditional deconvolution beamforming methods usually focus on beamforming intensity, which proves inadequate for handling coherent signals. To address this limitation, a deconvolution beamforming method for arbitrary arrays in beam domain is proposed, which is developed within the framework of compressive sensing. This method first uses conventional beamforming to obtain several complex output beams, and then applies the sparse Bayesian learning (SBL) algorithm to achieve deconvolution of complex output beams. This deconvolution process enhances the accuracy of direction of arrival (DOA) estimation for true targets. The proposed method effectively reduces computational complexity by optimizing the number of output beams generated during conventional beamforming. It is equally applicable to both uncoherent and coherent signals, outperforming conventional deconvolution beamforming methods. The simulation and experimental results demonstrate that the proposed method has azimuth resolution performance comparable to the traditional SBL beamforming in element domain, while outperforming conventional beamforming and minimum variance distortionless response (MVDR) method. When applied to short dense arrays, the proposed method achieves significantly lower computational complexity compared to traditional SBL in element domain, particularly when the number of output beams from conventional beamforming is much smaller than the number of array elements.
| [1] |
Baggeroer A B, Cox H. Passive sonar limits upon nulling multiple moving ships with large aperture arrays. Conference Record of the 33rd Asilomar Conference on Signals, Systems, and Computers, IEEE, Pacific Grove, CA, USA, 1999: 103–108
|
| [2] |
Ahmed N, Wang H, Raja M A Z, et al. Novel design of grey wolf optimization heuristics for high resolution direction of arrival estimation in acoustic plane waves. Wirel. Personal Commun., 2023; 128(4): 2507−2529 DOI: 10.1007/s11277-022-10057-w
|
| [3] |
McCloud M L, Scharf L L. A new subspace identification algorithm for high-resolution DOA estimation. IEEE Trans. Antennas Propag., 2002; 50(10): 1382−1390 DOI: 10.1109/TAP.2002.805244
|
| [4] |
Mahnaz A P, Sedigheh G. Mℓ1,2-MUSIC algorithm for DOA estimation of coherent sources. IET Signal Process., 2017; 11(4): 429−436 DOI: 10.1049/iet-spr.2016.0246
|
| [5] |
Xia J, Lu X, Chen W. Multi-channel deconvolution for forward-looking phase array radar imaging. Remote Sens., 2017; 9(7): 703 DOI: 10.3390/rs9070703
|
| [6] |
Pan X, Bao Y, Zhu Y, et al. Deconvolved conventional beamforming and adaptive cubature Kalman filter based distant speech perception system. IEEE Access, 2020; 8: 187948−187958 DOI: 10.1109/ACCESS.2020.3030814
|
| [7] |
Zhang Y, Lang S, Wang H, et al. Super-resolution algorithm based on Richardson–Lucy deconvolution for three-dimensional structured illumination microscopy. J. Opt. Soc. Am. A, 2019; 36(2): 173 DOI: 10.1364/JOSAA.36.000173
|
| [8] |
Shin H C, Prager R, Gomersall H, et al. Estimation of average speed of sound using deconvolution of medical ultrasound data. Ultrasound Med. Biol., 2010; 36(4): 623−636 DOI: 10.1016/j.ultrasmedbio.2010.01.011
|
| [9] |
Den Ouden O F C, Assink J D, Smets P S M, et al. CLEAN beamforming for the enhanced detection of multiple infrasonic sources. Geophys. J. Int., 2020; 221(1): 305−317 DOI: 10.1093/gji/ggaa010
|
| [10] |
Gade S, Hald J. Noise source identification with increased spatial resolution used in automotive industry. J. Acoust. Soc. Am., 2012; 131(4S): 3220 DOI: 10.1121/1.4708008
|
| [11] |
Sun D, Ma C, Mei J, et al. The deconvolved conventional beamforming for non-uniform line arrays. IEEE OCEANS, Kobe, Japan, 2018: 1–4
|
| [12] |
Brooks T F, Humphreys W M. A deconvolution approach for the mapping of acoustic sources (DAMAS) determined from phased microphone arrays. J. Sound Vib., 2006; 294: 856−879 DOI: 10.1016/j.jsv.2005.12.046
|
| [13] |
Dougherty R. Extensions of DAMAS and benefits and limitations of deconvolution in beamforming. 11th AIAA/CEAS Aeroacoustics Conference, AIAA, Monterey, California, 2005
|
| [14] |
Ehrenfried K, Koop L. Comparison of iterative deconvolution algorithms for the mapping of acoustic sources. AIAA J., 2007; 45(7): 1584−1595 DOI: 10.2514/1.26320
|
| [15] |
Yang T C. Deconvolved conventional beamforming for a horizontal line array. IEEE J. Oceanic Eng., 2018; 43(1): 160−172 DOI: 10.1109/JOE.2017.2680818
|
| [16] |
孙大军, 马超, 梅继丹, 等. 反卷积波束形成技术在水声阵列中的应用. 哈尔滨工程大学学报, 2020; 41(6): 860−869 DOI: 10.11990/jheu.201905013
|
| [17] |
梅继丹, 石文佩, 马超, 等. 近场反卷积聚焦波束形成声图测量. 声学学报, 2020; 45(1): 15−28 DOI: 10.15949/j.cnki.0371-0025.2020.01.002
|
| [18] |
Padois T, Berry A. Orthogonal matching pursuit applied to the deconvolution approach for the mapping of acoustic sources inverse problem. J. Acoust. Soc. Am., 2015; 138(6): 3678−3685 DOI: 10.1121/1.4937609
|
| [19] |
Nannuru S, Gerstoft P. 2D Beamforming on sparse arrays with sparse bayesian learning. IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, Brighton, United Kingdom, 2019: 4355–4359
|
| [20] |
Malioutov D, Cetin M, Willsky A S. A sparse signal reconstruction perspective for source localization with sensor arrays. IEEE Trans. Signal Process., 2005; 53(8): 3010−3022 DOI: 10.1109/TSP.2005.850882
|
| [21] |
Wang F, Tian X, Liu X, et al. Combination complex-valued Bayesian compressive sensing method for sparsity constrained deconvolution beamforming. IEEE Trans. Instrum. Meas., 2022; 71: 1−13 DOI: 10.1109/TIM.2022.3169537
|
| [22] |
Zoltowski M D, Kautz G M, Silverstein S D. Beamspace root-MUSIC. IEEE Trans. Signal Process., 1993; 41(1): 344 DOI: 10.1109/TSP.1993.193151
|
| [23] |
Gemba K L, Nannuru S, Gerstoft P, et al. Multi-frequency sparse Bayesian learning for robust matched field processing. J. Acoust. Soc. Am., 2017; 141(5): 3411−3420 DOI: 10.1121/1.4983467
|
| [24] |
Xu X L, Buckley K. An analysis of beam-space source localization. IEEE Trans. Signal Process., 1993; 41(1): 501 DOI: 10.1109/TSP.1993.193189
|
| [25] |
刘章孟. 基于信号空域稀疏性的阵列处理理论与方法. 博士学位论文, 长沙: 国防科技大学, 2012
|
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