关键词: 压缩感知, 非凸压缩感知, [?p]最小化, 迭代重加权最小二乘法

Abstract: Nonlinear compressive sensing reconstruction algorithm is one of three main studies. Based on the [?p]-norm detailed analysis of the existing iterative reweighted least squares optimization method, an improved iterative reweighted least square [?p]-norm minimization of nonconvex compressive sensing optimization algorithm is proposed. Experimental results show that the improved algorithm has a higher percentage of successful reconstruction and faster reconstruction speed, which can also greatly reduce the required number of measurements with the same sparsity and have great significance on reconstruction algorithms and practical application of compressive sensing.

Key words: compressive sensing, non-convex compressive sensing, [?p]minimization, iterative reweighted least squares