[1] Wu Feiyun, Zhou Yuehai, Tong Feng, Fang Shiliang, et al. Compressed sensing estimation of sparse underwateracoustic channels with a large time delay spread [J]. Journal of Southeast University (English Edition), 2014, 30 (3): 271-277. [doi:10.3969/j.issn.1003-7985.2014.03.003]

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Compressed sensing estimation of sparse underwateracoustic channels with a large time delay spread()

稀疏长时延水声信道的压缩感知估计

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

30

Issue:

2014 3

Page:

271-277

Research Field:

Mathematics, Physics, Mechanics

Publishing date:

2014-09-30

Info

Title:

Compressed sensing estimation of sparse underwateracoustic channels with a large time delay spread

稀疏长时延水声信道的压缩感知估计

Author(s):

Wu Feiyun¹;  Zhou Yuehai¹;  Tong Feng¹;  Fang Shiliang²

¹Key Laboratory of Underwater Acoustic Communication and Marine Information Technologyof Minister of Education, Xiamen University, Xiamen 361005, China
²Key Laboratory of Underwater Acoustic Signal Processing of Minister of Education, Southeast University, Nanjing 210096, China

伍飞云¹;  周跃海¹;  童峰¹;  方世良²

¹厦门大学水声通信与海洋信息技术教育部重点实验室, 厦门 361005; ²东南大学水声信号处理教育部重点实验室, 南京 210096

Keywords:

norm constraint;  sparse underwater acoustic channel;  compressed sensing

范数约束;  稀疏水声信道;  压缩感知

PACS:

TB567

DOI:

10.3969/j.issn.1003-7985.2014.03.003

Abstract:

The estimation of sparse underwater acoustic channels with a large time delay spread is investigated under the framework of compressed sensing. For these types of channels, the excessively long impulse response will significantly degrade the convergence rate and tracking capability of the traditional estimation algorithms such as least squares(LS), while excluding the use of the delay-Doppler spread function due to huge computational complexity. By constructing a Toeplitz matrix with a training sequence as the measurement matrix, the estimation problem of long sparse acoustic channels is formulated into a compressed sensing problem to facilitate the efficient exploitation of sparsity. Furthermore, unlike the traditional l₁ norm or exponent-based approximation l₀ norm sparse recovery strategy, a novel variant of approximate l₀ norm called AL0 is proposed, minimization of which leads to the derivation of a hybrid approach by iteratively projecting the steepest descent solution to the feasible set. Numerical simulations as well as sea trial experiments are compared and analyzed to demonstrate the superior performance of the proposed algorithm.

提出一种基于压缩感知框架下的长时延水声信道估计算法.用传统的自适应算法如最小二乘(LS)算法处理典型的长时延水声信道的估计问题时, 会导致其收敛速率下降, 即跟踪能力有限, 而使用时延多普勒函数则加大了计算量和复杂度.通过训练序列构建一个Toeplitz矩阵作为测量矩阵, 将长时延信道估计问题转为压缩感知问题, 并利用信道的稀疏结构特性进行稀疏估计.与传统的l₁范数或基于指数形式的近似l₀范数稀疏恢复策略不同, 所提出的是一种新的似l₀范数稀算法(简称AL0), 该算法通过融合最陡梯度和迭代投影寻优进行求解.仿真与海试数据结果验证了所提算法的优越性.

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Memo

Memo:

Biographies: Wu Feiyun(1984—), male, graduate; Tong Feng(corresponding author), male, doctor, professor, ftong@xmu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.11274259), the Open Project Program of the Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education(No.UASP1305).
Citation: Wu Feiyun, Zhou Yuehai, Tong Feng, et al. Compressed sensing estimation of sparse underwater acoustic channels with a large time delay spread[J].Journal of Southeast University(English Edition), 2014, 30(3):271-277.[doi:10.3969/j.issn.1003-7985.2014.03.003]

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