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针对传统的l2-范数信道估计精度低的问题, 提出了一种基于基追踪去噪(BPDN)的水声正交频分复用稀疏信道估计方法, 该方法针对水声信道的稀疏特性, 利用少量的观测值即可以很高的精度估计出信道冲激响应. 与贪婪追踪类算法相比, 基于BPDN算法的稀疏信号估计具有全局最优解, 采用l2-l1范数准则估计信号, 同时考虑了观测值含噪情况, 通过调整正则化参数控制估计信号稀疏度和残余误差之间的平衡. 仿真分析了导频分布、正则化参数等对BPDN 算法的影响以及BPDN算法与最小平方(LS)、正交匹配追踪(OMP)信道估计算法的性能. 湖试结果表明, 在稀疏信道下, 基于BPDN的信道估计方法明显优于LS和OMP信道估计方法.To solve the problem of poor performance of the traditional l2-norm channel estimation, a sparse channel estimation approach based on basis pursuit denoising (BPDN) is proposed in orthogonal frequency division multiplex underwater acoustic communication. Owing to the sparsity of the underwater acoustic channel, only a few observations are needed to recover the channel impulse response with a high accuracy. Compared with greedy pursuit algorithm, BPDN algorithm has the globally excellentest solution. The signal is estimated based on the l2-l1 norm rule and the observations containing the noise are considered. The regularization parameter can be changed to balance the signal's sparsity against the residual error. The influences of the pilot distribution and the regularization parameter on the BPDN algorithm are discussed in the simulation. The BPDN channel estimator is compared with the least square (LS) and also with orthogonal matching pursuit (OMP). The data collected from lake experiment show that the BPDN channel estimator outperforms the LS and OMP channel estimator over spare underwater acoustic channel.
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Keywords:
- basis pursuit denoising /
- orthogonal frequency division multiplex /
- sparse channel estimation /
- orthogonal matching pursuit
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[18] Weichang L, Preisig J C 2007 IEEE J. Ocean. Eng. 32 927
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[1] Qiao G, Liu S Z, Zhou F, Sun Z X 2012 2012 International Conference on Electrical Insulating Materials and Electrical Engineering Shenyang China, May 25-27, 2012 p1308
[2] Xu X K, Wang Z H, Zhou S L, Wan L 2012 J. Acoustic. Soc. Am. 131 4672
[3] Yin Y L, Zhou F, Qiao G, Liu S Z 2013 Acta Phys. Sin. 22 224302 (in Chinese) [尹艳玲, 周锋, 乔钢, 刘凇佐 2013 22 224302]
[4] Sang E, Xu X, Qiao G, Su J 2008 2008 International Conference on Neural Networks and Signal Processing Nanjing China June 7-11, 2008 p299
[5] Zhao Y B, Xiao H H, Li J C 2013 Mobile Commun. 2 70 (in Chinese) [赵砚博, 肖恒辉, 李炯城 2013 移动通信 2 70]
[6] Berger C R, Zhou S, Preisig J, Willett P 2010 IEEE Trans. Signal Proc. 58 1708
[7] Stojanovic M, Preisig J 2009 IEEE Commun. Mag. 47 84
[8] Donoho D 2006 IEEE Trans. Inform. Theory 52 1289
[9] Shi G M, Liu D H, Gao D H, Liu Z, Lin J, Wang L J 2009 Acta Electron. Sin. 37 1070 (in Chinese) [时光明, 刘丹华, 高大化, 刘哲, 林杰, 王良君 2009 电子学报 37 1070]
[10] Liu S Z, Qiao G, Yin Y L 2013 Acta Phys. Sin. 62 144303 (in Chinese) [刘凇佐, 乔钢, 尹艳玲 2013 62 144303]
[11] Berger C R, Shengli Z, Preisig J, Willett P 2010 IEEE Trans. Signal Proces. 58 1708
[12] He X Y, Song R F, Zhou K Q 2010 J. Nanjing Univ. Posts Telecom. (Natural Science) 30 60 (in Chinese) [何雪云, 宋荣方, 周克琴2010 南京邮电大学学报 (自然科学版) 30 60]
[13] Wang X L, Wang C L 2005 Appl. Elec. Tech. 8 19 (in Chinese) [汪雄良, 王春玲 2005 电子技术应用 8 19]
[14] Ning F L, He B J, Wei J 2013 Acta Phys. Sin. 62 174212 (in Chinese) [宁方立, 何碧静, 韦娟 2013 62 174212]
[15] Yaakov T, David L D 2006 Signal Process 86 549
[16] Scott S C, David L D, Michael A S 2001 SIAM Rev. 43 129
[17] Li Z F, Yan J W 2010 Microcomput. Appl. 31 12 (in Chinese) [李卓凡, 闫敬文 2010 微计算机应用 31 12]
[18] Weichang L, Preisig J C 2007 IEEE J. Ocean. Eng. 32 927
[19] Wright S J, Nowak R D, Figueiredo M A T 2009 IEEE Trans. Signal Proces. 57 2479
[20] Fuchs J 2004 IEEE Trans. Inform. Theory 50 1341
[21] Seung Jean Kim, Koh K, Lustig M, Stephen B, Dimitry G 2007 IEEE J. Select. Topics in Signal Process. 1 606
[22] Candes E J, Tao T 2005 IEEE Trans. Inform. Theory 51 4203
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