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文章编号： 1672-6987（2022）02-0121-06； DOI： 10.16351/j.1672-6987.2022.02.018
张海霞， 杜子俊， 王景景*(青岛科技大学 信息科学技术学院，山东 青岛 266061）
摘要： 多径匹配追踪算法在水声信道稀疏估计中具有较好的估计精度，但该算法需要信道稀疏度的先验信息，且计算复杂度大。本工作提出一种基于交叉验证与正则化相结合的多径匹配追踪算法，将其用于水声信道估计。交叉验证提供算法的停止准则，不需要信道的稀疏度和噪声水平的先验信息，并检查算法是否过拟合，提高了估计的准确性。正则化用来进一步筛选候选集，减少计算复杂性及存储开销。仿真结果表明：与原始多径匹配追踪算法相比复杂度大大降低，具有更好的性能，并且不需要信道稀疏度的先验信息。
关键词： 水声通信； 压缩感知； 稀疏信道估计； 交叉验证
中图分类号： TN 929.3文献标志码： A
引用格式： 张海霞， 杜子俊， 王景景. 基于CV-RMMP重构算法的水声稀疏信道估计［J］. 青岛科技大学学报(自然科学版), 2022, 43(2): 121-126.
ZHANG Haixia， DU Zijun， WANG Jingjing. Underwater acoustic sparse channel estimation based on CV-RMMP reconstruction algorithm［J］. Journal of Qingdao University of Science and Technology（Natural Science Edition), 2022， 43（2）： 121-126.
Underwater Acoustic Sparse Channel Estimation Based on
CV-RMMP Reconstruction Algorithm
ZHANG Haixia， DU Zijun， WANG Jingjing
(College of Information Science and Technology，Qingdao University of Science and Technology，Qingdao 266061，China)
Abstract: Multipath matching pursuit algorithm has better estimation accuracy in sparse estimation of underwater acoustic channel, but this algorithm requires channel sparsity as a priori information and is computationally complex. Therefore, a multipath matching pursuit based on the combination of cross validation and regularization algorithm is proposed in this paper, and used for underwater acoustic channel estimation. Cross validation provides a stopping criterion for the algorithm, does not require prior information on the sparsity and noise level of the channel, and checks whether the algorithm is over-fitting, which improves the accuracy of the estimation. Regularization is used to further filter the candidate set, reducing computational complexity and storage overhead. The simulation results show that compared with the original multipath matching pursuit algorithm, the complexity of this algorithm is reduced, it has better performance, and does not require channel sparsity as a priori information.
Key words: underwater acoustic communication; compressed sensing; sparse channel estimation; cross validation
收稿日期： 2021-03-24
基金项目： 国家自然科学基金重点类项目（U1806201）.
作者简介： 张海霞(1995—), 女, 硕士研究生.*通信联系人.
