针对浅海脉冲噪声导致水声OFDM通信系统信道估计性能下降的问题，提出一种基于导频增益因子的TMSBL信道和脉冲噪声联合估计方法。该方法将联合估计问题转换为多测量向量压缩感知问题，引入导频增益因子放大导频字典矩阵，利用TMSBL算法实现对信道和脉冲噪声联合矩阵的稀疏重构，然后从矩阵中分离信道和脉冲噪声。仿真结果表明，在强脉冲环境下，所提算法相较于TMSBL联合估计方法，信道估计的NMSE降低了87.20%，获得了更高的估计精度；运行时间下降了61.44%，显著降低了算法的复杂度。因此，引入导频增益因子放大导频字典矩阵，能有效地提高联合估计方法的性能，为水声信道和脉冲噪声联合估计提供参考。
To address the degradation of channel estimation performance in underwater OFDM communication systems caused by impulsive noise in shallow waters, a joint estimation method for channel and impulsive noise based on a pilot gain factor is proposed. This method transforms the joint estimation problem into a multiple measurement vector compressed sensing problem by introducing a pilot gain factor to amplify the pilot dictionary matrix. The TMSBL algorithm is used to achieve sparse reconstruction of the joint matrix of channel and impulsive noise, and the channel and impulsive noise are then separated from the matrix. Simulation results show that under strong impulsive noise conditions, the proposed algorithm reduces the NMSE of channel estimation by 87.20% compared to the TMSBL joint estimation method, achieving higher estimation accuracy, and reduces runtime by 61.44%, significantly lowering the algorithm’s complexity. Therefore, introducing the pilot gain factor to amplify the pilot dictionary matrix effectively improves the performance of the joint estimation method, providing a valuable reference for the joint estimation of underwater acoustic channels and impulsive noise.
2025,47(14): 112-120 收稿日期：2024-7-11
DOI：10.3404/j.issn.1672-7649.2025.14.017
分类号：TN929.3
基金项目：国家自然科学基金资助项目（61761048）；云南省基础研究专项面上项目（202101AT070132）；云南民族大学硕士研究生科研创新基金项目（2024SKY122）
作者简介：冉艳玲(2000-),女,硕士研究生,研究方向为水声信号处理
参考文献：
[1] SUN D, WU J, HONG X, et al. Iterative double-differential direct-sequence spread spectrum reception in underwater acoustic channel with time-varying Doppler shifts[J]. The Journal of the Acoustical Society of America, 2023, 153(2): 1027-1041.
[2] 邢传玺, 张东玉, 宋扬, 等. 利用字典学习方法的声速剖面反演研究[J]. 声学技术, 2021, 40(6): 750-756.
XING C X, ZHANG D Y, SONG Y, et al. Research on inversion of sound speed profile using dictionary learning method[J]. Technical Acoustics, 2021, 40(6): 750-756.
[3] 徐文, 鄢社锋, 季飞, 等. 海洋信息获取、传输、处理及融合前沿研究评述[J]. 中国科学: 信息科学, 2016, 46(8): 1053-1085.
XU W, YAN S F, JI F, et al. Marine information gathering, transmission, processing, and fusion: current status and future trends[J]. Sciencetia Sinica Informationis, 2016, 46(8): 1053-1085.
[4] 孟熹亚, 刘增力. 面向浅海水声通信的TB-GOMP信道估计算法[J]. 兵器装备工程学报, 2023, 44(5): 223-229.
MENG X Y, LIU Z L. TB-GOMP channel estimation algorithm for shallow underwater acoustic communication[J]. Journal of Ordnance Equipment Engineering, 2023, 44(5): 223-229.
[5] JIA S, ZOU S, ZHANG X, et al. Multi-block Sparse Bayesian learning channel estimation for OFDM underwater acoustic communication based on fractional Fourier transform[J]. Applied Acoustics, 2022, 192: 108721.
[6] 杨永嘉, 冯海泓, 李记龙, 等. OFDM水声通信多普勒估计与跟踪方法[J]. 声学技术, 2024, 43(3): 403-411.
YANG Y J, FENG H H, LI J L, et al. Doppler estimation and tracking method for OFDM underwater acoustic communication[J]. Technical Acoustics, 2024, 43(3): 403-411.
[7] COTTER S F, RAO B D. Sparse channel estimation via matching pursuit with application to equalization[J]. IEEE Transactions on communications, 2002, 50(3): 374-377.
[8] QIAO G, SONG Q, MA L, et al. A low-complexity orthogonal matching pursuit based channel estimation method for time-varying underwater acoustic OFDM systems[J]. Applied Acoustics, 2019, 148: 246-250.
[9] WIPF D P, RAO B D. Sparse Bayesian learning for basis selection[J]. IEEE Transactions on Signal processing, 2004, 52(8): 2153-2164.
[10] WIPF D P, RAO B D. An empirical Bayesian strategy for solving the simultaneous sparse approximation problem[J]. IEEE Transactions on Signal Processing, 2007, 55(7): 3704-3716.
[11] ZHANG Z, RAO B D. Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(5): 912-926.
[12] QIAO G, SONG Q, MA L, et al. Sparse Bayesian learning for channel estimation in time-varying underwater acoustic OFDM communication[J]. IEEE Access, 2018, (6): 56675-56684.
[13] 洪丹阳, 王巍, 尹力, 等. 改进的时序多重稀疏贝叶斯学习冰下水声信道估计方法[J]. 声学学报, 2022, 47(5): 591-602.
HONG D Y, WANG W, YIN L, et al. An improved temporal multiple sparse Bayesian learning under-ice acoustic channel estimation method[J]. Acta acustica, 2022, 47(5): 591-602.
[14] QIAO G, SONG Q, MA L, et al. Channel prediction based temporal multiple sparse bayesian learning for channel estimation in fast time-varying underwater acoustic OFDM communications[J]. Signal Processing, 2020, 175: 107668.
[15] 高世杰, 王彪, 朱雨男, 等. 基于最大相关熵准则的水下生物脉冲噪声消除方法[J]. 声学技术, 2021, 40(5): 717-722.
GAO S J, WANG B, ZHU Y N, et al. A method of eliminating underwater biological impulse noise based on maximum correntropy criterion[J]. Technical Acoustics, 2021, 40(5): 717-722.
[16] PELEKANAKIS K, CHITRE M. Robust equalization of mobile underwater acoustic channels[J]. IEEE Journal of Oceanic Engineering, 2015, 40(4): 775-784.
[17] LIN J, NASSAR M, EVANS B L. Impulsive noise mitigation in powerline communications using sparse Bayesian learning[J]. IEEE Journal on Selected Areas in Communications, 2013, 31(7): 1172-1183.
[18] 殷敬伟, 高新博, 韩笑, 等. 稀疏贝叶斯学习水声信道估计与脉冲噪声抑制方法[J]. 声学学报, 2021, 46(6): 813-824.
YIN J W, GAO X B, HAN X, et al. Underwater acoustic channel estimation and impulsive noise mitigation based on sparse Bayesian learning[J]. ACTA ACUSTICA, 2021, 46(6): 813-824.
[19] DIMITROV S, SINANOVIC S, HAAS H. Clipping noise in OFDM-based optical wireless communication systems[J]. IEEE Transactions on Communications, 2012, 60(4): 1072-1081.
[20] ZHIDKOV S V. Analysis and comparison of several simple impulsive noise mitigation schemes for OFDM receivers[J]. IEEE Transactions on Communications, 2008, 56(1): 5-9.
[21] CHEN P, RONG Y, NORDHOLM S, et al. Joint channel estimation and impulsive noise mitigation in underwater acoustic OFDM communication systems[J]. IEEE Transactions on Wireless Communications, 2017, 16(9): 6165-6178.
[22] 吕新荣, 李有明, 余明宸. OFDM系统的信道与脉冲噪声的联合估计方法[J]. 通信学报, 2018, 39(3): 191-198.
LYU X R, LI Y M, YU M C. Joint channel and impulsive noise estimation method for OFDM systems[J]. Journal on Communications, 2018, 39(3): 191-198.
[23] 吕新荣, 李有明, 国强. MIMO-OFDM系统的信道与脉冲噪声联合估计方法[J]. 通信学报, 2021, 42(12): 54-64.
LYU X R, LI Y M, GUO Q. Joint channel and impulsive noise estimation method for MIMO-OFDM systems[J]. Journal on Communications, 2021, 42(12): 54-64.
[24] XING C X, RAN Y L, TAN G Z, et al. Impulse noise mitigation and channel estimation method in OFDM systems based on TMSBL[J]. IEEE Access, 2024, (12): 123376–123387.
[25] 谭钢, 鄢社锋, 叶子豪, 等. OFDM系统迭代脉冲噪声抑制与信道估计方法[J]. 系统工程与电子技术, 2024, 46(8): 2841-2849.
TAN G, YAN S F, YE Z H, et al. Iterative impulsive noise mitigation and channel estimation method for OFDM system[J]. Systems Engineering and Electronics, 2024, 46(8): 2841-2849.
[26] BANERJEE, SHARBARI, MONIKA A. On the performance of underwater communication system in noise with Gaussian mixture statistics[C]//2014 Twentieth National Conference on Communications (NCC), IEEE, 2014.
[27] SONG G L, GUO X Y, LI H, et al. The α stable distribution model in ocean ambient noise[J]. Chinese Journal of Acoustics, 2021, 40(1): 63-79.
