Underwater Acoustic Imaging via Online Bayesian Compressive Beamforming

被引：1
作者：
Guo, Qijia [1 ,2 ,3 ]
Yang, Siyun [1 ,2 ,3 ]
Zhou, Tian [1 ,2 ,3 ]
Wang, Zhongmin [4 ]
Cui, Hong-Liang [5 ]
机构：
[1] Harbin Engn Univ, Acoust Sci & Technol Lab, Harbin 150001, Peoples R China
[2] Harbin Engn Univ, Coll Underwater Acoust Engn, Harbin 150001, Peoples R China
[3] Harbin Engn Univ, Key Lab Marine Informat Acquisit & Secur, Minist Ind & Informat Technol, Harbin 150001, Peoples R China
[4] Qilu Univ Technol, Inst Automat, Shandong Acad Sci, Jinan 250014, Peoples R China
[5] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Guangdong, Peoples R China
来源：
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2022年 / 19卷
基金：
中国国家自然科学基金;
关键词：
Imaging; Convergence; Bayes methods; Correlation; Kalman filters; Uncertainty; Filtering; Acoustic imaging; Kalman filtering (KF); Rauch-Tung-Striebel (RTS) smoothing; sawtooth lag; sparse Bayesian learning (SBL); RECOVERY;
D O I：
10.1109/LGRS.2022.3180751
中图分类号：
P3 [地球物理学]; P59 [地球化学];
学科分类号：
0708 ; 070902 ;
摘要：
Beamforming is extensively used in underwater acoustic imaging systems. As a high-resolution variety, a Bayesian compressive beamformer treats the acoustic echo of each snapshot independently and achieves enhanced recovery performance. However, its associated computational cost is extremely high compared with conventional beamformers, and its iterative implementation makes it difficult to be used online. To overcome these obstacles, an online Kalman filtering sparse Bayesian learning (online-KSBL) based compressive beamformer is proposed in this work, which is non-iterative and computationally efficient for a continual working scenario, where a long-term imaging task is conducted underwater. Imposing independent assumptions on snapshots, the online-SBL approach can be derived from online-KSBL. The hyperparameters of the model are estimated recursively, following efficient procedures that are implemented approximately with a sawtooth lag scheme. For the underwater imaging scenario, the correlation among snapshots is exploited and inferred from online-KSBL, and it is found that online-SBL performs competitively with online-KSBL, and it is sufficient to employ online-SBL without considering the snapshot correlation, retaining low complexity, as demonstrated by experimental results.
引用
收藏
页数：5
相关论文
共 15 条
[1]  
Chalasani Rakesh, 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), P7188, DOI 10.1109/ICASSP.2014.6854995
[2]   Compressive Beamforming Based on Multiconstraint Bayesian Framework [J].
Li, Chao ;
Zhou, Tian ;
Guo, Qijia ;
Cui, Hong-Liang .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2021, 59 (11) :9209-9223
[3]   Dynamic Filtering of Time-Varying Sparse Signals via l1 Minimization [J].
Charles, Adam S. ;
Balavoine, Aurele ;
Rozell, Christopher J. .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2016, 64 (21) :5644-5656
[4]   Multisnapshot Sparse Bayesian Learning for DOA [J].
Gerstoft, Peter ;
Mecklenbrauker, Christoph F. ;
Xenaki, Angeliki ;
Nannuru, Santosh .
IEEE SIGNAL PROCESSING LETTERS, 2016, 23 (10) :1469-1473
[5]   Type I and Type II Bayesian Methods for Sparse Signal Recovery Using Scale Mixtures [J].
Giri, Ritwik ;
Rao, Bhaskar .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2016, 64 (13) :3418-3428
[6]   Bayesian compressive sensing [J].
Ji, Shihao ;
Xue, Ya ;
Carin, Lawrence .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2008, 56 (06) :2346-2356
[7]   A Noniterative Online Bayesian Algorithm for the Recovery of Temporally Correlated Sparse Vectors [J].
Joseph, Geethu ;
Murthy, Chandra R. .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2017, 65 (20) :5510-5525
[8]  
Karseras E, 2013, INT CONF ACOUST SPEE, P6546, DOI 10.1109/ICASSP.2013.6638927
[9]  
Liu HQ, 2015, 2015 IEEE CHINA SUMMIT & INTERNATIONAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING, P1022, DOI 10.1109/ChinaSIP.2015.7230559
[10]   Joint Approximately Sparse Channel Estimation and Data Detection in OFDM Systems Using Sparse Bayesian Learning [J].
Prasad, Ranjitha ;
Murthy, Chandra R. ;
Rao, Bhaskar D. .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2014, 62 (14) :3591-3603
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