关键词: 压缩感知, 分段正交匹配追踪, 粒子群优化

Abstract: Signal reconstruction is one of the core technologies of compressed sensing, and the reconstruction accuracy and time-consuming directly affects its application effect. Nowadays, Stagewise Orthogonal Matching Pursuit （StOMP） algorithm has been widely used for short running time, but its reconstruction accuracy is unsatisfactory. To make up for the defects of the StOMP algorithm, this paper presents a variant of StOMP, called backtracking-based adaptive and inertia weight index decreasing particle swarm optimization-based StOMP（ba-IWPSO-StOMP） algorithm. As an extension of the StOMP algorithm, in each iteration, the proposed ba-IWPSO-StOMP algorithm incorporates a backtracking technique to select atoms by the second screening, then uses the IWPSO algorithm to optimize atoms in the measurement matrix. Through these modifications, the ba-IWPSO-StOMP algorithm achieves superior reconstruction accuracy and less times of iteration compared with other OMP-type algorithms. Moreover, unlike its predecessors, the ba-IWPSO-StOMP algorithm does not require to know the sparsity level in advance. The experiments demonstrate the performance of ba-IWPSO-StOMP algorithm is superior to several other OMP-type algorithms.

Key words: compressed sensing, Stagewise Orthogonal Matching Pursuit（StOMP）, Particle Swarm Optimization（PSO）