基于优化离散差分进化算法的压缩感知信号重构

Compressed sensing signal reconstruction based on optimized discrete differential evolution algorithm

针对传统压缩感知重构算法严重依赖稀疏度、重构精准度不高的缺陷,提出了一种基于优化离散差分进化(ODDE)算法,对进化种群进行分析,在实现种群有效聚类的同时提高了种群学习进化的针对性和科学性。重新定义了差分进化粒子的编码方式和进化机制,并将优化后的离散差分进化算法应用于压缩感知重构方法中。将稀疏度未知信号等效为粒子编码,通过种群迭代进化实现了稀疏信号的精确重构。仿真结果表明,与StOMP等传统重构算法相比,本文方法可以显著提高重构精度、降低重构时间。

In order to overcome the shortcomings of traditional compressed sensing reconstruction algorithms, such as heavy dependence on sparseness and low reconstruction accuracy, an Optimized Discrete Differential Evolution(ODDE) algorithm is proposed to analyze the evolutionary population.ODDE can improve the learning and evolution of the population while realizing effective clustering. The differential evolution particle coding method and evolution mechanism are redefined, and the ODDE is applied to the compressed sensing reconstruction method. The sparse unknown signal is equivalent to the particle coding, and the sparse signal is reconstructed accurately through population iterative evolution. The simulation results show that compared with traditional reconstruction algorithms such as StOMP, the reconstruction accuracy of this method is significantly improved and the reconstruction time is reduced.