系统工程与电子技术 ›› 2024, Vol. 46 ›› Issue (10): 3279-3284.doi: 10.12305/j.issn.1001-506X.2024.10.05

• 电子技术 • 上一篇    

多路径支撑集回溯贪婪重构算法

田文飚1,2,*, 芮国胜2, 张嵩2, 张海波2, 王林2   

  1. 1. 海军航空大学航空作战勤务学院, 山东 烟台 264001
    2. 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:2023-02-18 出版日期:2024-09-25 发布日期:2024-10-22
  • 通讯作者: 田文飚
  • 作者简介:田文飚(1987—), 男, 副教授, 博士, 主要研究方向为压缩感知、通信信号处理
    芮国胜(1968—), 男, 教授, 博士研究生导师, 博士, 主要研究方向为压缩感知、现代滤波理论、微弱信号检测
    张嵩(1979—), 男, 副教授, 博士, 主要研究方向为微弱信号检测、航空通信技术
    张海波(1983—), 男, 副教授, 博士, 主要研究方向为多输入多输出通信、航空电子技术
    王林(1985—), 男, 讲师, 博士, 主要研究方向为航空通信技术
  • 基金资助:
    国家自然科学基金(41606117);国家自然科学基金(41476089);国家自然科学基金(61671016)

Multipath backtracking greedy pursuit algorithm

Wenbiao TIAN1,2,*, Guosheng RUI2, Song ZHANG2, Haibo ZHANG2, Lin WANG2   

  1. 1. School of Aviation Operations and Support, Naval Aviation University, Yantai 264001, China
    2. Signal and Information Processing Provincial Key Laboratory in Shandong, Naval Aviation University, Yantai 264001, China
  • Received:2023-02-18 Online:2024-09-25 Published:2024-10-22
  • Contact: Wenbiao TIAN

摘要:

针对现有压缩感知贪婪算法容易陷于局部最优、过拟合等问题, 提出一种稀疏恢复算法, 称为多路径支撑集回溯贪婪重构(multipath backtracking greedy pursuit, MBGP)算法。该算法以最小残差为重构目标, 对候选原子展开多条路径同时搜索, 且每次筛选多个原子, 通过回溯过程剔除误选的原子。基于有限等距性质给出MBGP算法重构信号的充分条件, 以确保其从测量值精确恢复任何K-稀疏信号, 并通过信号重构能力来评估MBGP算法的性能。数值实验结果表明, 该算法在相同信号条件下, 能够在采样数更少、稀疏度更大的场合下精确重构信号, 且性能更逼近理想Oracle-最小二乘估计器。

关键词: 压缩感知, 信号恢复, 匹配追踪, 子空间追踪, 剪枝, 回溯, 贪婪算法

Abstract:

For the existing compressed sensing (CS) greedy algorithm, it is easy to fall into problems such as local optimum and overfitting. A sparse recovery algorithm called multipath backtracking greedy pursuit (MBGP) is proposed. MBGP algorithm searches the signal support set and iteratively examines multiple candidate support set estimates at the same time, and finally selects the one that minimizes the reconstruction residual. Based on the restricted isometry property, the sufficient conditions for the MBGP algorithm to reconstruct the signal are given to ensure that it can accurately recover any K-sparse signal from the measured value. The performance of the MBGP algorithm is evaluated by the signal reconstruction ability. Numerical experimental results show that the algorithm can accurately reconstruct signals with fewer samples and greater sparsity under the same signal conditions, and its performance is closer to the ideal Oracle-least square estimator.

Key words: compressed sensing (CS), signal recovery, matching pursuit, subspace pursuit, pruning, backtracking, greedy algorithm

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