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AN IMPROVED SPARSITY ADAPTIVE MATCHING PURSUIT ALGORITHM FOR COMPRESSIVE SENSING BASED ON REGULARIZED BACKTRACKING 被引量:3
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作者 Zhao Ruizhen Ren Xiaoxin +1 位作者 Han Xuelian Hu Shaohai 《Journal of Electronics(China)》 2012年第6期580-584,共5页
Sparsity Adaptive Matching Pursuit (SAMP) algorithm is a widely used reconstruction algorithm for compressive sensing in the case that the sparsity is unknown. In order to match the sparsity more accurately, we presen... Sparsity Adaptive Matching Pursuit (SAMP) algorithm is a widely used reconstruction algorithm for compressive sensing in the case that the sparsity is unknown. In order to match the sparsity more accurately, we presented an improved SAMP algorithm based on Regularized Backtracking (SAMP-RB). By adapting a regularized backtracking step to SAMP algorithm in each iteration stage, the proposed algorithm can flexibly remove the inappropriate atoms. The experimental results show that SAMP-RB reconstruction algorithm greatly improves SAMP algorithm both in reconstruction quality and computational time. It has better reconstruction efficiency than most of the available matching pursuit algorithms. 展开更多
关键词 Compressive sensing Reconstruction algorithm sparsity adaptive Regularized back-tracking
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A sparsity adaptive compressed signal reconstruction based on sensing dictionary 被引量:1
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作者 SHEN Zhiyuan WANG Qianqian CHENG Xinmiao 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2021年第6期1345-1353,共9页
Signal reconstruction is a significantly important theoretical issue for compressed sensing.Considering the situation of signal reconstruction with unknown sparsity,the conventional signal reconstruction algorithms us... Signal reconstruction is a significantly important theoretical issue for compressed sensing.Considering the situation of signal reconstruction with unknown sparsity,the conventional signal reconstruction algorithms usually perform low accuracy.In this work,a sparsity adaptive signal reconstruction algorithm using sensing dictionary is proposed to achieve a lower reconstruction error.The sparsity estimation method is combined with the construction of the support set based on sensing dictionary.Using the adaptive sparsity method,an iterative signal reconstruction algorithm is proposed.The sufficient conditions for the exact signal reconstruction of the algorithm also is proved by theory.According to a series of simulations,the results show that the proposed method has higher precision compared with other state-of-the-art signal reconstruction algorithms especially in a high compression ratio scenarios. 展开更多
关键词 compressed sensing signal reconstruction adaptive sparsity estimation sensing dictionary
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Improving the reconstruction efficiency of sparsity adaptive matching pursuit based on the Wilkinson matrix 被引量:3
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作者 Rasha SHOITAN Zaki NOSSAIR +1 位作者 I.I.IBRAHIM Ahmed TOBAL 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2018年第4期503-512,共10页
Sparsity adaptive matching pursuit(SAMP)is a greedy reconstruction algorithm for compressive sensing signals.SAMP reconstructs signals without prior information of sparsity and presents better reconstruction performan... Sparsity adaptive matching pursuit(SAMP)is a greedy reconstruction algorithm for compressive sensing signals.SAMP reconstructs signals without prior information of sparsity and presents better reconstruction performance for noisy signals compared to other greedy algorithms.However,SAMP still suffers from relatively poor reconstruction quality especially at high compression ratios.In the proposed research,the Wilkinson matrix is used as a sensing matrix to improve the reconstruction quality and to increase the compression ratio of the SAMP technique.Furthermore,the idea of block compressive sensing(BCS)is combined with the SAMP technique to improve the performance of the SAMP technique.Numerous simulations have been conducted to evaluate the proposed BCS-SAMP technique and to compare its results with those of several compressed sensing techniques.Simulation results show that the proposed BCS-SAMP technique improves the reconstruction quality by up to six decibels(d B)relative to the conventional SAMP technique.In addition,the reconstruction quality of the proposed BCS-SAMP is highly comparable to that of iterative techniques.Moreover,the computation time of the proposed BCS-SAMP is less than that of the iterative techniques,especially at lower measurement fractions. 展开更多
关键词 Block compressive sensing sparsity adaptive matching pursuit Greedy algorithm Wilkinson matrix
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