全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
兵工学报  2014 

可适应稀疏度变化的非均匀范数约束水声信道估计算法

DOI: 10.3969/j.issn.1000-1093.2014.09.025, PP. 1503-1509

Keywords: 声学,最小均方算法,非均匀范数,信道估计,范数约束

Full-Text   Cite this paper   Add to My Lib

Abstract:

?对于具有典型时频双重扩展特性的水声信道,利用其稀疏分布特性在估计算法中引入范数约束可提高信道估计性能。但当水声信道多径稀疏度变化时,经典的l0或l1范数约束由于缺乏对不同稀疏模式的适应性,将导致性能下降。通过引入非均匀范数约束自适应算法并对其进行收敛性分析,针对水声信道稀疏度变化利用该算法通过非均匀范数的形式提高适应性。不同接收深度水声信道的仿真及海上实验结果表明,该算法相对经典的l0或l1范数约束算法有较明显的性能改善。

References

[1]  [1] Li W, Preisig J C. Estimation of rapidly time-varying sparse channels[J]. IEEE Journal of Oceanic Engineering, 2007, 32(4): 927-939.
[2]  [6] Rao B D, Delgado K K. An affine scaling methodology for best basis selection[J]. IEEE Transactions on Signal Processing, 1999, 47(1): 187-200.
[3]  [7] Naylor P A, Cui J, Brookes M. Adaptive algorithms for sparse echo cancellation[J]. Signal Processing, 2006, 86(6): 1182-1192.
[4]  [8] 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.
[5]  [9] 童峰,许肖梅,方世良. 一种单频水声信号多径时延估计算法[J]. 声学学报, 2008, 33(1): 62-68.
[6]  [10] 陈东升, 李霞, 方世良, 等. 基于参数模型和混合优化的时变水声信道跟踪[J]. 东南大学学报:自然科学版, 2010, 40(3):459-463.
[7]  [11] Zeng W J, Xu W. Fast estimation of sparse doubly spread acoustic channels[J]. Journal of the Acoustical Society of America, 2012, 131(1): 303-317.
[8]  [12] Konstantinos P, Mandar C. New sparse adaptive algorithms based on the natural gradient and the l0-norm[J]. IEEE Journal of Oceanic Engineering, 2013, 38(2): 323-332.
[9]  [13] Gu Y, Jin Y, Mei S. l0 norm constraint LMS algorithm for sparse system identification[J]. IEEE Signal Processing Letters, 2009, 16(9): 774-777.
[10]  [14] Jin J, Gu Y, Mei S. A stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(2): 409-420.
[11]  [15] 曲庆, 金坚, 谷源涛. 用于稀疏系统辨识的改进 l0-LMS 算法[J]. 电子与信息学报, 2011, 33(3): 604-609.
[12]  [16] Su G, Jin J, Gu Y, et al. Performance analysis of l0 norm constraint least mean square algorithm[J]. IEEE Transactions on Signal Processing, 2012, 60(5): 2223-2235.
[13]  [17] Chen Y, Gu Y, Hero A O. Sparse LMS for system identification[C]∥IEEE International Conference on Acoustics, Speech and Signal Processing. Taibei,Taiwan: IEEE, 2009:3125-3128.
[14]  [18] Shi K, Shi P. Adaptive sparse Volterra system identification with l0-norm penalty[J]. Signal Processing, 2011, 91(10): 2432-2436.
[15]  [19] Shi K, Shi P. Convergence analysis of sparse LMS algorithms with l1-norm penalty based on white input signal[J]. Signal Processing, 2010, 90(12): 3289-3293.
[16]  [20] Wu F Y, Tong F. Gradient optimization p-norm-like constraint LMS algorithm for sparse system estimation[J]. Signal Processing, 2013, 93(4): 967-971.
[17]  [21] Wu F Y, Tong F. Non-uniform norm constraint LMS algorithm for sparse system identification[J]. IEEE Communications Letters, 2013, 17(2): 385-388.
[18]  [2] Stojanovic M. Retrofocusing techniques for high rate acoustic communications[J]. Journal of the Acoustical Society of America, 2005, 117(3): 1173-1185.
[19]  [3] Stojanovic M. Efficient processing of acoustic signals for high-rate information transmission over sparse underwater channels[J]. Physical Communication, 2008, 1(2): 146-161.
[20]  [4] Kalouptsidis N, Mileounis G, Babadi B, et al. Adaptive algorithms for sparse system identification[J]. Signal Processing, 2011, 91(8): 1910-1919.
[21]  [5] Angelosante D, Bazerque J A, Giannakis G B. Online adaptive estimation of sparse signals: where RLS meets the l1-norm[J]. IEEE Transactions on Signal Processing, 2010, 58(7): 3436-3447.
[22]  [22] Kostas S, Paolo C, Michele Z. The throughput of underwater networks: analysis and validation using a ray tracing simulator[J]. IEEE Transactions on Wireless Communications, 2013, 12(3): 1108-1117.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133