Joint Bayesian and Greedy Recovery for Compressive Sensing  

作　　者：LI Jia 

机构地区：[1]China Electronics Technology Group Corporation 54 th Research Institute,Shijiazhuang 050081,China [2]Hebei Key Laboratory of Electromagnetic Spectrum Cognition and Control,Shijiazhuang 050081,China

出　　处：《Chinese Journal of Electronics》2020年第5期945-951,共7页电子学报（英文版）

摘　　要：Greedy algorithms are widely used for sparse recovery in compressive sensing.Conventional greedy algorithms employ the inner product vector of signal residual and sensing matrix to det ermine the support,which is based on the assumption that the indexes of the larger-magnitude entries of the inner product vector are more likely to be contained in the correct supports.However,this assumption may be not valid when the number of measurements is not sufficient,leading to the selection of an incorrect support.To improve the accuracy of greedy recovery,we propose a novel greedy algorithm to recover sparse signals from incomplete measurements.The entries of a sparse signal are modelled by the type-II Laplacian prior,such that the k indexes of the correct support are indicated by the largest k variance hyperparameters of the entries.Based on the proposed model,the supports can be recovered by approximately estimating the hyperparameters via the maximum a posteriori process.Simulation results demonstrate that the proposed algorithm out performs the conventional greedy algorithms in terms of recovery accuracy,and it exhibits satisfactory recovery speed.

关 键 词：Sparse recovery Compressive sensing Greedy algorithm Laplacian prior. 

分 类 号：TN911.7[电子电信—通信与信息系统]