长时延扩展水声信道的联合稀疏恢复估计

周跃海; 曹秀岭; 陈东升; 童峰

doi:10.11959/j.issn.1000-436x.2016043

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长时延扩展水声信道的联合稀疏恢复估计

学术论文 | 更新时间：2024-06-05

- 长时延扩展水声信道的联合稀疏恢复估计
- Jointing sparse recovery estimation algorithm of underwater acoustic channels with long time delay spread
- 通信学报 2016年37卷第2期页码：166-173
- 作者机构：
  
  厦门大学水声通信与海洋信息技术教育部重点实验室，福建厦门361005
- 作者简介：
  
  [ "周跃海（1987-），男，福建龙岩人，厦门大学博士生，主要研究方向为水声通信、水声信号处理、压缩感知信道估计、水声通信方案硬件设计和实现等。" ]
  [ "曹秀岭（1975-），男，山东菏泽人，厦门大学博士生，主要研究方向为水声通信、水声信号处理等。" ]
  [ "陈东升（1975-），男，福建莆田人，厦门大学助理教授，主要研究方向为水声通信、水声信号处理、水声网络等。" ]
  [ "童峰（1973-），男，福建龙岩人，厦门大学教授、博士生导师，主要研究方向为水声通信、水声网络和水声信号处理等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(11274259);国家自然科学基金资助项目(11574258)
- DOI：10.11959/j.issn.1000-436x.2016043
  中图分类号： TN929.3
- 网络出版日期：2016-02，
  
  纸质出版日期：2016-02-15
- 稿件说明：
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周跃海, 曹秀岭, 陈东升, 等. 长时延扩展水声信道的联合稀疏恢复估计[J]. 通信学报, 2016,37(2):166-173.

Yue-hai ZHOU, Xiu-ling CAO, Dong-sheng CHEN, et al. Jointing sparse recovery estimation algorithm of underwater acoustic channels with long time delay spread[J]. Journal on communications, 2016, 37(2): 166-173.
周跃海, 曹秀岭, 陈东升, 等. 长时延扩展水声信道的联合稀疏恢复估计[J]. 通信学报, 2016,37(2):166-173. DOI： 10.11959/j.issn.1000-436x.2016043.

Yue-hai ZHOU, Xiu-ling CAO, Dong-sheng CHEN, et al. Jointing sparse recovery estimation algorithm of underwater acoustic channels with long time delay spread[J]. Journal on communications, 2016, 37(2): 166-173. DOI： 10.11959/j.issn.1000-436x.2016043.

摘要

对具有长时延扩展的水声信道，传统的信道估计算法如最小二乘法将在大量零值抽头产生严重的估计噪声，导致估计性能下降，同时信道估计时所需的较高估计器阶数大大提高了运算复杂度。压缩感知信道估计方法可有效利用多径稀疏特性改善性能，但需采用较大的训练序列长度以保证稀疏恢复精度，由此导致额外的系统开销。利用水声信道多径稀疏结构在数据块间存在的相关性，建立基于分布式压缩感知的长时延水声信道联合稀疏模型，从而可利用同步正交匹配追踪算法进行联合重构，以进一步减小系统的训练序列开销，提高估计性能。最后通过仿真和海上实验验证了所提方法的有效性。

Abstract

Efficient estimation of underwater acoustic channels with a large time ay spread was addressed. For the conventional channel estimation methods such as LS

this type of channel estimation would produce serious estimation noise in zero-value taps which lead to poor performance of channel estimation. At the same time

a large time delay spread posed significant difficulties such as large channel order and the corresponding huge computation complexity. Compressed sensing (CS)channel estimation algorithm offered a solution to this problem by exploiting the sparsity of channel to improve the estimation performance. However to ensure acceptable estimation performance

a long training sequence was needed

which unfortunately would cause additional overhead. A method was proposed which exploiting the joint correlation of sparse multipath structure between adjacent data blocks to deal with the estimation of long time delay channels under the framework of distributed compressed sensing (DCS).Thus the large time delay underwater acoustic channels can be jointly reconstructed by the simultaneous orthogonal matching t (SOMP)algorithm to fa-cilitate the system overhead reduction and estimation ance improvement. Simulation as well as the sea trial re-sults indicate the effectiveness of the proposed method.

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references

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