电子学报 ›› 2020, Vol. 48 ›› Issue (5): 990-996.DOI: 10.3969/j.issn.0372-2112.2020.05.021

所属专题: 压缩感知

• 学术论文 • 上一篇    下一篇

基于稀疏贝叶斯学习的时域流信号鲁棒动态压缩感知算法

董道广, 芮国胜, 田文飚, 张洋, 张海波   

  1. 海军航空大学信号与信息处理山东省重点实验室, 山东烟台 264001
  • 收稿日期:2019-04-15 修回日期:2019-09-06 出版日期:2020-05-25 发布日期:2020-05-25
  • 作者简介:董道广 男,1990年9月出生,山东济南人.海军航空大学信息与通信工程博士研究生,主要研究方向为Bayesian统计学习和蒸发波导反演.E-mail:sikongyu@yeah.net;芮国胜 男,1968年3月出生,山东烟台人.海军航空大学教授、博士生导师,主要研究方向为混沌通信系统和现代滤波理论.E-mail:ruigs@sina.com
  • 基金资助:
    国家自然科学基金(No.41476089,No.41606117,No.61671016)

A Robust Dynamic Compressive Sensing Algorithm for Streaming Signals in Time Domain Based on Sparse Bayesian Learning

DONG Dao-guang, RUI Guo-sheng, TIAN Wen-biao, ZHANG Yang, ZHANG Hai-bo   

  1. Signal and Information Processing Key Laboratory in Shandong, Navy Aviation University, Yantai, Shandong 264001, China
  • Received:2019-04-15 Revised:2019-09-06 Online:2020-05-25 Published:2020-05-25

摘要: 块效应和未知且时变的噪声强度会降低时域流信号动态稀疏重构的性能,为解决该问题,本文基于重叠正交变换和稀疏贝叶斯学习框架,提出一种对时域流信号进行动态压缩感知的鲁棒稀疏贝叶斯学习重构算法.该算法在消除块效应的同时,能够处理噪声强度未知且时变情形下的动态稀疏重构问题,相比现有的流信号稀疏贝叶斯学习算法具有更强的抗噪鲁棒性.尽管现有的时域流信号压缩感知的有效算法并不多,但实验表明,本文算法的重构信误比和重构成功率均明显高于现有的基于稀疏贝叶斯学习的流信号重构算法和基于L1-同伦的流信号重构算法,且达到相同的重构成功率所需的观测数目少于另两种算法,计算量和运行效率则与稀疏贝叶斯学习算法相近.

关键词: 块效应, 流信号, 稀疏贝叶斯学习, 动态重构

Abstract: Performance of dynamic sparse recovery for streaming signals in time domain will degrade for the existence of blocking artifacts and unknown time-varying noise intensity.To solve the above problems,a robust sparse Bayesian learning algorithm for dynamic compressive sensing of streaming signals in time domain is proposed based on the framework of lapped orthogonal transform and sparse Bayesian learning.In addition to eliminating the blocking artifacts,the proposed algorithm handles dynamic sparse Bayesian learning problems effectively under conditions of unknown time-varying noise intensity,which has better robustness against existing sparse Bayesian learning algorithms for streaming signals.Though there are not many existing effective algorithms for compressed sensing of streaming signals,experiments show that the proposed algorithm has obviously larger reconstruction signal-to-noise ratio and higher success rates for reconstruction than existing recovery algorithms for streaming signals based on sparse Bayesian learning or L1-homotopy; also,the measurement number required for particular success rates is obviously less than that of the other two algorithms,the computation cost and running time is approximately the same with the existing sparse Bayesian learning algorithm.

Key words: blocking artifacts, streaming signals, sparse Bayesian learning, dynamic reconstruction

中图分类号: