Jiang Weihua, Zheng Siyuan, Zhou Yuehai, Tong F, Kastner Ryan
The Key Laboratory of Underwater Acoustic Communication and Marine Information Technology of the Minister of Education, Xiamen University, Xiamen, Fujian, 361005, China.
Department of Computer Science and Engineering, University of California San Diego, La Jolla, California 92093, USA.
J Acoust Soc Am. 2018 Jun;143(6):3997. doi: 10.1121/1.5042355.
While it has been recognized that the multipath structure of the underwater acoustic (UWA) channel offers the potential for compressed sensing (CS) sparsity exploitation, the rapidly time varying arrivals induced by highly dynamic surfaces unfortunately pose significant difficulties to channel estimation. From the viewpoint of underwater acoustic propagation, with the exception of the highly time varying arrivals caused by dynamic surface, generally there exist relatively stationary or slowly changing arrivals caused by direct path or bottom reflection, which imply the adoption of a discriminate estimation method to handle sparse components with different time variation scale. By modeling the time varying UWA channels as a sparse set consisting of constant and time-varying supports, in this paper, estimation of time varying UWA channel is transformed into a problem of dynamic compressed sensing sparse recovery. The combination of a Kalman filter and compressed sensing is adopted to pursue the solution of it. Numerical simulations demonstrate the superiority of the proposed algorithm. In the form of a channel-estimation-based decision-feedback equalizer, the experimental results with the field data obtained in a shallow water acoustic communication experiment indicate that the proposed dynamic compressed sensing algorithm outperforms classic algorithms as well as CS algorithms.
虽然人们已经认识到水下声学（UWA）信道的多径结构为利用压缩感知（CS）稀疏性提供了潜力，但由高动态表面引起的快速时变到达信号不幸地给信道估计带来了重大困难。从水下声学传播的角度来看，除了由动态表面引起的高度时变到达信号外，通常还存在由直达路径或海底反射引起的相对静止或缓慢变化的到达信号，这意味着需要采用一种判别估计方法来处理具有不同时变尺度的稀疏分量。通过将时变UWA信道建模为由恒定和时变支撑集组成的稀疏集，本文将时变UWA信道估计问题转化为动态压缩感知稀疏恢复问题。采用卡尔曼滤波器和压缩感知相结合的方法来求解该问题。数值模拟证明了所提算法的优越性。在基于信道估计的判决反馈均衡器形式下，利用浅海水声通信实验中获得的现场数据进行的实验结果表明，所提动态压缩感知算法优于经典算法以及CS算法。
