Generalized subspace pursuit for signal recovery from multiple-measurement vectors

Joe Mei Feng, Chia-Han Lee

研究成果: Conference contribution同行評審

31 引文 斯高帕斯(Scopus)

摘要

Extension from the single-measurement vector (SMV) problem to the multiple-measurement vectors (MMV) problem is critical for compressed sensing (CS) in many applications. By increasing the number of measurement vectors, a k-jointly-sparse signal can be recovered with less stringent requirements on the signal sparsity. Simultaneous orthogonal matching pursuit (SOMP), an MMV extension of the orthogonal matching pursuit (OMP) algorithm, is a widely used algorithm for the MMV problem. We noticed that for the SMV problems, the subspace pursuit (SP) algorithm outperforms OMP, so it was expected that the extension of SP to its MMV version, called simultaneous subspace pursuit (SSP) here, will easily outperform SOMP. However, we found that this direct approach does not allow the signal recovery rate to scale with the increase in the number of measurement vectors. To circumvent this, in this paper we propose the generalized subspace pursuit (GSP) algorithm, in which the number of columns to be selected in each of subspace pursuit iteration is properly chosen. Extensive simulation results confirm that the proposed GSP algorithm outperforms SOMP and SSP under various sampling matrix settings with noiseless and noisy measurements. In addition, we show the restricted isometry property (RIP)-guarantee that leads to the convergence of the proposed GSP algorithm and the uniqueness of the recovered signal.

原文English
主出版物標題2013 IEEE Wireless Communications and Networking Conference, WCNC 2013
頁面2874-2878
頁數5
DOIs
出版狀態Published - 2013
事件2013 IEEE Wireless Communications and Networking Conference, WCNC 2013 - Shanghai, China
持續時間: 7 4月 201310 4月 2013

出版系列

名字IEEE Wireless Communications and Networking Conference, WCNC
ISSN(列印)1525-3511

Conference

Conference2013 IEEE Wireless Communications and Networking Conference, WCNC 2013
國家/地區China
城市Shanghai
期間7/04/1310/04/13

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