关键词: 压缩感知, 冗余字典, 正交匹配追踪算法, 量子粒子群算法

Abstract: The Orthogonal Matching Pursuit（OMP） algorithm which is based on the idea of greedy iteration is one of the compressed sensing signal reconstruction algorithm. To reduce the OMP computational complexity, quantum particle swarm（QPSO） algorithm which is more powerful in searching for global optimal solution is applied to optimize the matching course of orthogonal matching pursuit algorithm （QPSO-OMP）. According to OMP algorithm features the atomic weight secondary matching is introduced to improve the reconstruction accuracy of QPSO-OMP. Simulation results show that the orthogonal matching pursuit algorithm with secondary matching based on the quantum particle swarm algorithm performs better than that based on the particle swarm algorithm in accurate reconstruction probability while with low complexity.

Key words: compressed sensing, over-complete dictionary, orthogonal matching pursuit algorithm, quantum particle swarm algorithm