部分范数约束的稀疏恢复算法及其在单载波水声数据遥测中的应用

伍飞云; 杨坤德; 童峰

doi:10.11959/j.issn.1000-436x.2018099

您当前的位置：

首页 >

文章列表页 >

部分范数约束的稀疏恢复算法及其在单载波水声数据遥测中的应用

学术论文 | 更新时间：2024-06-05

- 部分范数约束的稀疏恢复算法及其在单载波水声数据遥测中的应用
- Partial-norm-constrained sparse recovery algorithm and its application on single carrier underwater-acoustic-data telemetry
- 通信学报 2018年39卷第6期页码：127-132
- 作者机构：
  
  1. 西北工业大学航海学院，陕西西安 710072
  2. 西北工业大学海洋声学信息感知工业和信息化部重点实验室，陕西西安 710072
  3. 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：
  
  [ "伍飞云（1984-），男，江西宜春人，西北工业大学助理教授，主要研究方向为信号处理、压缩感知、水声通信等。" ]
  [ "杨坤德（1974-），男，四川邻水人，西北工业大学教授、博士生导师，主要研究方向为信号处理、压缩感知、微波传输等。" ]
  [ "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、水声信号处理等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61701405);中央高校基本科研业务费专项资金资助项目(3102017OQD007);中国博士后科学基金资助项目(2017M613208)
- DOI：10.11959/j.issn.1000-436x.2018099
  中图分类号： TN929.3
- 网络首发：2018-06，
  
  纸质出版：2018-06-25
- 稿件说明：
移动端阅览
伍飞云, 杨坤德, 童峰. 部分范数约束的稀疏恢复算法及其在单载波水声数据遥测中的应用[J]. 通信学报, 2018,39(6):127-132.

Feiyun WU, Kunde YANG, Feng TONG. Partial-norm-constrained sparse recovery algorithm and its application on single carrier underwater-acoustic-data telemetry[J]. Journal on Communications, 2018, 39(6): 127-132.
伍飞云, 杨坤德, 童峰. 部分范数约束的稀疏恢复算法及其在单载波水声数据遥测中的应用[J]. 通信学报, 2018,39(6):127-132. DOI： 10.11959/j.issn.1000-436x.2018099.

Feiyun WU, Kunde YANG, Feng TONG. Partial-norm-constrained sparse recovery algorithm and its application on single carrier underwater-acoustic-data telemetry[J]. Journal on Communications, 2018, 39(6): 127-132. DOI： 10.11959/j.issn.1000-436x.2018099.

摘要

对于单载波水声数据压缩与恢复问题，压缩感知能以较低能耗获得信号压缩与恢复效果。但压缩感知核心目标是直接求最小l

范数，该问题表现为NP难问题，因此，常将其转化为求l

范数约束最小化问题，而求l

范数约束最小化的稀疏解精度有限。基于此，推导出基于部分范数约束的稀疏信号恢复算法，该算法通过部分范数约束在拉格朗日求解中增加一个零吸引项，从而动态分配稀疏抽头的软阈值。同时，该算法用于实际海上数据的遥测，结合离散余弦变换（DCT），可将单载波水声数据恢复精度提高。

Abstract

To solve the problem of single carrier underwater-acoustic-data telemetry

compressive sensing (CS) provides competitive performance of compression and recovery with low energy consumption.The primary objective of CS is to minimize the l

norm

which is an NP hard problem.Hence

the common methods were transferred to minimize l

norm.However

norm minimization provided a limited accuracy.A partial-norm-constraint (PNC) based sparse signal recovery method was derived

which adopted PNC as a zero attraction in a Lagrange method

to distribute the soft threshold for the non-zero taps.The prop

osed method is used for at-sea data telemetry.Combining with DCT

the proposed method improves the recovery accuracy.

关键词

Keywords

references

CLIMENT S , SANCHEZ A , CAPELLA J V , et al . Underwater acoustic wireless sensor networks:advances and future trends in physical,MAC and routing layers [J ] . Sensors , 2014 , 14 ( 1 ): 795 - 833 .

HEIDEMANN J , YE W , WILLS J , et al . Research challenges and applications for underwater sensor networking [C ] // Wireless Communications and NETWORKING Conference . 2006 : 228 - 235 .

HEIDEMANN J , STOJANOVIC M , ZORZI M . Underwater sensor networks:applications,advances and challenges [J ] . Philosophical Transactions , 2012 , 370 ( 1958 ): 158 - 175 .

ZHUO J , ZHANG Y , LIU X H , et al . An underwater acoustic data compression method using improved threshold integer wavelet and LZW algorithm [J ] . Technical Acoustics , 2015 , 34 : 115 - 120 .

MAMAGHANIAN H , KHALED N , ATIENZA D , et al . Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes [J ] . IEEE Transactions on Biomedical Engineering , 2011 , 58 ( 9 ): 2456 - 2466 .

DONOHO D L . Compressed sensing [J ] . IEEE Transactions on Information Theory , 2006 , 52 ( 4 ): 1289 - 1306 .

DONOHO D L , ELAD M , TEMLYAKOV V N . Stable recovery of sparse overcomplete representations in the presence of noise [J ] . IEEE Transactions on Information Theory , 2006 , 52 ( 1 ): 6 - 18 .

BENESTY J , PALEOLOGU C , CIOCHINA S . Proportionate adaptive filters from a basis pursuit perspective [J ] . IEEE Signal Processing Letters , 2010 , 17 ( 12 ): 985 - 988 .

WRIGHT S J , NOWAK R D , FIGUEIREDO M A T . Sparse reconstruction by separable approximation [J ] . IEEE Transactions on Signal Processing , 2009 , 57 ( 7 ): 2479 - 2493 .

BLUMENSATH T . Accelerated iterative hard thresholding [J ] . Signal Processing , 2012 , 92 ( 3 ): 752 - 756 .

MALLAT S G , ZHANG Z . Matching pursuits with time-frequency dictionaries [J ] . IEEE Transactions on Signal Processing , 1993 , 41 ( 12 ): 3397 - 3415 .

TROPP J A , GILBERT A C . Signal recovery from random measurements via orthogonal matching pursuit [J ] . IEEE Transactions on Information Theory , 2007 , 53 ( 12 ): 4655 - 4666 .

WU F Y , TONG F . Non-uniform norm constraint LMS algorithm for sparse system identification [J ] . IEEE Communications Letters , 2013 , 17 ( 2 ): 385 - 388 .

LI Y , WANG Y , JIANG T . Norm-adaption penalized least mean square/fourth algorithm for sparse channel estimation [J ] . Signal Processing , 2016 , 128 ( C ): 243 - 251 .

WANG C , ZHANG Y , WEI Y , et al . A new $l_0$-LMS algorithm with adaptive zero attractor [J ] . IEEE Communications Letters , 2015 , 19 ( 12 ): 2150 - 2153 .

WU F Y , TONG F . Mean-square analysis of the gradient projection sparse recovery algorithm based on non-uniform norm [J ] . Neurocomputing , 2017 , 223 : 103 - 106 .

SARTIPI M , FLETCHER R . Energy-efficient data acquisition in wireless sensor networks using compressed sensing [C ] // Data Compression Conference . 2011 : 223 - 232 .

浏览量

1377

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于蜂窝覆盖的超大规模MIMO系统近场码本设计

基于稀疏贝叶斯学习的大规模多用户检测算法

基于压缩感知的FBMC/OQAM系统信道估计方法

基于压缩感知与矩阵补全技术的WSN数据收集算法

大规模MIMO系统稀疏度自适应信道估计算法