Target Echo Enhancement under Moving Platform Reverberation Using Low-Rank and Sparse Decomposition

Yan WANG; Yuliang HE; Longhao QIU; Nan ZOU

doi:10.11999/JEIT200143

Volume 43 Issue 7

Jul. 2021

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Yan WANG, Yuliang HE, Longhao QIU, Nan ZOU. Target Echo Enhancement under Moving Platform Reverberation Using Low-Rank and Sparse Decomposition[J]. Journal of Electronics & Information Technology, 2021, 43(7): 1978-1984. doi: 10.11999/JEIT200143

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Yan WANG, Yuliang HE, Longhao QIU, Nan ZOU. Target Echo Enhancement under Moving Platform Reverberation Using Low-Rank and Sparse Decomposition[J]. Journal of Electronics & Information Technology, 2021, 43(7): 1978-1984. doi: 10.11999/JEIT200143

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Target Echo Enhancement under Moving Platform Reverberation Using Low-Rank and Sparse Decomposition

doi: 10.11999/JEIT200143

1.
Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China
2.
College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China

Funds: The National Key R&D Plan (2017YFC0306900), The National Defense Basic Scientific Research (JCKY2019604B001)

Received Date: 2020-03-03
Rev Recd Date: 2020-10-12

Available Online: 2020-12-07

Publish Date: 2021-07-10

Abstract

Abstract

For the underwater moving platforms, the near-range filtering of active sonar is seriously affected by reverberation disturbance. Usually, the real target echo will be masked by numerous reverberation highlights, which will greatly increase the false alarm rate of subsequent detection. Among adjacent periods of the bearing-time-record from array processing in some scenarios, this paper utilizes the potential coherent structure of reverberation, and then assumes that reverberation component satisfies the low-rank property. In addition, the relative motion may assume that target echoes of interest are irrelevant and sparse. Accordingly, the bearing-time-record can be decomposed as low-rank reverberation, sparse moving target echo and noise components. To suppress reverberation and enhance target echoes, the Accelerated Proximal Gradient(APG) and Fast Data Projection Method(FDPM) are proposed to realize offline and online decomposition, respectively. The experimental results validate the assumed models, and both approaches can effectively enhance target echoes.
- Active sonar reverberation,
- Target echo enhancement,
- Low-rank and sparse decomposition,
- Bearing-time-record

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References(16)

References

[1]	GE Fengxiang, CHEN Ying, and LI Weichang. Target detecton and tracking via structured convex optimization[C]. Proceedings of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, USA, 2017: 426–430. doi: 10.1109/ICASSP.2017.7952191.
[2]	ZOU Hui and XUE Lingzhou. A selective overview of sparse principal component analysis[J]. Proceedings of the IEEE, 2018, 106(8): 1311–1320. doi: 10.1109/JPROC.2018.2846588
[3]	LI Haiyan, YIN Fan, and LI Chao. A high-accuracy target tracking method and its application in acoustic engineering[C]. Proceedings of the IEEE 4th International Conference on Signal and Image Processing (ICSIP), Wuxi, China, 2019: 690–694. doi: 10.1109/SIPROCESS.2019.8868499.
[4]	BOUWMANS T, JAVED S, ZHANG Hongyang, et al. On the applications of robust PCA in image and video processing[J]. Proceedings of the IEEE, 2018, 106(8): 1427–1457. doi: 10.1109/JPROC.2018.2853589
[5]	VAN LUONG H, DELIGIANNIS N, SEILER J, et al. Compressive online robust principal component analysis via n-ℓ₁ minimization[J]. IEEE Transactions on Image Processing, 2018, 27(9): 4314–4329. doi: 10.1109/TIP.2018.2831915
[6]	杨依忠, 汪鹏飞, 胡雄楼, 等. 基于鲁棒主成分分析的运动目标检测优化算法[J]. 电子与信息学报, 2018, 40(6): 1309–1315. doi: 10.11999/JEIT170789 YANG Yizhong, WANG Pengfei, HU Xionglou, et al. Moving object detection optimization algorithm based on robust principal component analysis[J]. Journal of Electronics &Information Technology, 2018, 40(6): 1309–1315. doi: 10.11999/JEIT170789
[7]	ZHENG Zhi, HUANG Yixiao, WANG Wenqin, et al. Spatial smoothing PAST algorithm for DOA tracking using difference coarray[J]. IEEE Signal Processing Letters, 2019, 26(11): 1623–1627. doi: 10.1109/LSP.2019.2942146
[8]	WU Qiang, ZHENG Jiansheng, DONG Zhicheng, et al. An improved adaptive subspace tracking algorithm based on approximated power iteration[J]. IEEE Access, 2018, 6: 43136–43145. doi: 10.1109/ACCESS.2018.2863557
[9]	DOUKOPOULOS X G and MOUSTAKIDES G V. Fast and stable subspace tracking[J]. IEEE Transactions on Signal Processing, 2008, 56(4): 1452–1465. doi: 10.1109/tsp.2007.909335
[10]	HSU D, KAKADE S M, and ZHANG Tong. Robust matrix decomposition with sparse corruptions[J]. IEEE Transactions on Information Theory, 2011, 57(11): 7221–7234. doi: 10.1109/TIT.2011.2158250
[11]	HE Jun, BALZANO L, and LUI J C S. Online robust subspace tracking from partial information[EB/OL]. https://arxiv.org/pdf/1109.3827v2.pdf, 2011.
[12]	TOH K C and YUN S. An accelerated proximal gradient algorithm for nuclear norm regularized least squares problems[J]. Pacific Journal of Optimization, 2010, 6(3): 615–640.
[13]	LIN Zhouchen, CHEN Minming, and MA Yi. The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices[EB/OL]. https://arxiv.org/pdf/1009.5055v3.pdf, 2010.
[14]	BECK A and TEBOULLE M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems[J]. SIAM Journal on Imaging Sciences, 2009, 2(1): 183–202. doi: 10.1137/080716542
[15]	LIN Zhouchen, ARVIND G, JOHN W, et al. Fast convex optimization algorithms for exact recovery of a corrupted low-rank matrix[R]. Coordinated Science Laboratory Report no. UILU-ENG-09-2214, DC-246, 2009. doi: 10.1017/S0025315400020749.
[16]	LI Weichang, SUBRAHMANYA N, and XU Feng. Online subspace and sparse filtering for target tracking in reverberant environment[C]. Proceedings of the IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM), Hoboken, USA, 2012: 329–332. doi: 10.1109/SAM.2012.6250502.