TAILIEUCHUNG - Báo cáo sinh học: " Research Article Optimized Projection Matrix for Compressive Sensing"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài:Research Article Optimized Projection Matrix for Compressive Sensing | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 560349 8 pages doi 2010 560349 Research Article Optimized Projection Matrix for Compressive Sensing Jianping Xu Yiming Pi and Zongjie Cao School of Electronic Engineering University of Electronic Science and Technology of China Chengdu 610054 China Correspondence should be addressed to Jianping Xu xujianping1982@ Received 27 September 2009 Revised 26 April 2010 Accepted 22 June 2010 Academic Editor A. Enis Cetin Copyright 2010 Jianping Xu et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Compressive sensing CS is mainly concerned with low-coherence pairs since the number of samples needed to recover the signal is proportional to the mutual coherence between projection matrix and sparsifying matrix. Until now papers on CS always assume the projection matrix to be a random matrix. In this paper aiming at minimizing the mutual coherence a method is proposed to optimize the projection matrix. This method is based on equiangular tight frame ETF design because an ETF has minimum coherence. It is impossible to solve the problem exactly because of the complexity. Therefore an alternating minimization type method is used to find a feasible solution. The optimally designed projection matrix can further reduce the necessary number of samples for recovery or improve the recovery accuracy. The proposed method demonstrates better performance than conventional optimization methods which brings benefits to both basis pursuit and orthogonal matching pursuit. 1. Introduction Compressive sensing CS 1-3 has received much attention as it has shown promising results in many applications. CS is an emerging framework stating that signals which have a sparse representation on an appropriate basis can be .

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