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Volume 43 Issue 3
Mar.  2021
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Siyuan CANG, Xueli SHENG, Hang DONG, Longxiang GUO. Deconvolution-based Target Echo High-resolution Time Delay Estimation Technique Using Active Sonar[J]. Journal of Electronics & Information Technology, 2021, 43(3): 842-849. doi: 10.11999/JEIT200649
Citation: Siyuan CANG, Xueli SHENG, Hang DONG, Longxiang GUO. Deconvolution-based Target Echo High-resolution Time Delay Estimation Technique Using Active Sonar[J]. Journal of Electronics & Information Technology, 2021, 43(3): 842-849. doi: 10.11999/JEIT200649

Deconvolution-based Target Echo High-resolution Time Delay Estimation Technique Using Active Sonar

doi: 10.11999/JEIT200649
Funds:  The National Natural Science Foundation of China (51979061, 51779061), The National Key R&D Program of China (2018YFC1405902)
  • Received Date: 2020-08-03
  • Rev Recd Date: 2021-02-06
  • Available Online: 2021-02-19
  • Publish Date: 2021-03-22
  • In view of enhancing the time delay estimation resolution for the target echo in a complex shallow-water environment, thus improving the target detection ability of the active sonar system. A high-resolution time delay estimation technique is proposed to detect the underwater target based on sparse representation theory and deconvolution framework. Firstly, the Toeplitz operator is introduced here to construct a dictionary matrix using the various time delayed replicing of the transmitting signal. The estimated time-delay value can be found in the desired sparse vector solution. Secondly, the Alternating Direction Method of Multipliers (ADMM) is implemented to calculate the optimal solution globally. Thirdly, the reweighted iteration approach is explored to control the regularization parameter, thus suppressing the impact of the multipath channel. The arrival time of the echo can be decoupled to obtain a high-resolution time delay result. The simulated and experimental data verify that the proposed deconvolution-based time delay estimation technique can be used to detect the underwater target in shallow-water acoustic multipath channels. The resolution of the estimated time-delay result can achieve 0.056 ms.
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  • HAGUE D A and BUCK J R. An experimental evaluation of the generalized sinusoidal frequency modulated waveform for active sonar systems[J]. The Journal of the Acoustical Society of America, 2019, 145(6): 3741–3755. doi: 10.1121/1.5113581
    YIN Jingwei, MEN Wei, HAN Xiao, et al. Integrated waveform for continuous active sonar detection and communication[J]. IET Radar, Sonar & Navigation, 2020, 14(9): 1382–1390. doi: 10.1049/iet-rsn.2020.0099
    ZHANG Ting, YANG T C, and XU Wen. Bistatic localization of objects in very shallow water[J]. IEEE Access, 2019, 7: 180640–180651. doi: 10.1109/ACCESS.2019.2947851
    YANG T C. Deconvolved conventional beamforming for a horizontal line array[J]. IEEE Journal of Oceanic Engineering, 2018, 43(1): 160–172. doi: 10.1109/JOE.2017.2680818
    MA Lin, GULLIVER T A, ZHAO Anbang, et al. Underwater broadband source detection using an acoustic vector sensor with an adaptive passive matched filter[J]. Applied Acoustics, 2019, 148: 162–174. doi: 10.1016/j.apacoust.2018.12.023
    HAMA Y and OCHIAI H. Performance analysis of matched-filter detector for MIMO spatial multiplexing over rayleigh fading channels with imperfect channel estimation[J]. IEEE Transactions on Communications, 2019, 67(5): 3220–3233. doi: 10.1109/TCOMM.2019.2892758
    PADOIS T, DOUTRES O, and SGARD F. On the use of modified phase transform weighting functions for acoustic imaging with the generalized cross correlation[J]. The Journal of the Acoustical Society of America, 2019, 145(3): 1546–1555. doi: 10.1121/1.5094419
    CHANDRAN V, ELGAR S, and NGUYEN A. Detection of mines in acoustic images using higher order spectral features[J]. IEEE Journal of Oceanic Engineering, 2002, 27(3): 610–618. doi: 10.1109/JOE.2002.1040943
    BENESTY J, CHEN Jingdong, and HUANG Yiteng. Time-delay estimation via linear interpolation and cross correlation[J]. IEEE Transactions on Speech and Audio Processing, 2004, 12(5): 509–519. doi: 10.1109/TSA.2004.833008
    SALVATI D and CANAZZA S. Adaptive time delay estimation using filter length constraints for source localization in reverberant acoustic environments[J]. IEEE Signal Processing Letters, 2013, 20(5): 507–510. doi: 10.1109/LSP.2013.2253319
    SHAO Zhenfeng, WANG Lei, WANG Zhongyuan, et al. Remote sensing image super-resolution using sparse representation and coupled sparse autoencoder[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(8): 2663–2674. doi: 10.1109/JSTARS.2019.2925456
    崔维嘉, 张鹏, 巴斌. 基于贝叶斯自动相关性确定的稀疏重构正交频分复用信号时延估计算法[J]. 电子与信息学报, 2019, 41(10): 2318–2324. doi: 10.11999/JEIT181181

    CUI Weijia, ZHANG Peng, and BA Bin. Sparse reconstruction OFDM delay estimation algorithm based on Bayesian automatic relevance determination[J]. Journal of Electronics &Information Technology, 2019, 41(10): 2318–2324. doi: 10.11999/JEIT181181
    王洪雁, 于若男. 基于稀疏和低秩恢复的稳健DOA估计方法[J]. 电子与信息学报, 2020, 42(3): 589–596. doi: 10.11999/JEIT190263

    WANG Hongyan and YU Ruonan. Sparse and low rank recovery based robust DOA estimation method[J]. Journal of Electronics &Information Technology, 2020, 42(3): 589–596. doi: 10.11999/JEIT190263
    MENG Xiangxia, LI Xiukun, JAKOBSSON A, et al. Sparse estimation of backscattered echoes from underwater object using integrated dictionaries[J]. The Journal of the Acoustical Society of America, 2018, 144(6): 3475–3484. doi: 10.1121/1.5083830
    XIA Zhi, LI Xiukun, and MENG Xiangxia. High resolution time-delay estimation of underwater target geometric scattering[J]. Applied Acoustics, 2016, 114: 111–117. doi: 10.1016/j.apacoust.2016.07.016
    HAUPT J, BAJWA W U, RAZ G, et al. Toeplitz compressed sensing matrices with applications to sparse channel estimation[J]. IEEE Transactions on Information Theory, 2010, 56(11): 5862–5875. doi: 10.1109/TIT.2010.2070191
    TIBSHIRANI R. Regression shrinkage and selection via the lasso[J]. Journal of the Royal Statistical Society: Series B (Methodological) , 1996, 58(1): 267–288. doi: 10.1111/j.2517-6161.1996.tb02080.x
    GRANT M and BOYD S. CVX: Matlab software for disciplined convex programming, version 2.1[OL]. http://cvxr.com/, 2014.
    BOYD S, PARIKH N, CHU E, et al. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers[M]. Boston: Now Publishers Inc., 2011. doi: 10.1561/2200000016.
    CANDÈS E J, WAKIN M B, and BOYD S P. Enhancing sparsity by reweighted ℓ1 minimization[J]. Journal of Fourier Analysis and Applications, 2008, 14(5/6): 877–905. doi: 10.1007/s00041-008-9045-x
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