支撑驱动的非凸压缩感知恢复算法

Support driven recovery algorithm for non-convex compressed sensing

为解决带噪压缩感知信号恢复的难题,提出一种基于支撑驱动的恢复算法,分2步完成稀疏信号的恢复.1使用阈值基追踪方法获取信号支撑信息,并生成权值矩阵与所需其他参数.2使用迭代重加权算法求解非凸目标函数.在理论分析的基础上,与现有7种有竞争力的算法(含oracle估计器)进行了数值仿真比较.结果证明,文中算法以较低的运算量实现了高概率恢复.

A novel method is presented for the purpose of recovering sparse high dimensional signals from few linear measurements,especially in the noisy case.The proposed method works in the following two steps： 1The support of signal is approximately identified via Thresholded Basis Pursuit（TBP）,the weighting matrix and parameters needed for the next step are also computed;2 The Iteratively Reweighted Lp Minimization（IRLp）procedure is used to solve the non-convex objective function.As theoretic interpretation and simulation results show,lower computational complexity is required for the proposed Support Driven IRLp（SDIRLp）algorithm for high probability recovery,in comparison to 7analogous methods（including an oracle estimator）.