支撑驱动的非凸压缩感知恢复算法

doi:10.3969/j.issn.1001-2400.2016.02.001

Abstract

Abstract:

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; ②The 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 7 analogous methods(including an oracle estimator).

Key words: compressed sensing, basis pursuit, iteratively reweighted Lp minimization

WANG Feng;XIANG Xin;YI Kechu;XIONG Lei. Support driven recovery algorithm for non-convex compressed sensing[J].Journal of Xidian University, 2016, 43(2): 1-5+28.

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Support driven recovery algorithm for non-convex compressed sensing

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