Fast reconstruction algorithm for perturbed compressive sensing based on total least-squares and proximal splitting

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• 作者：Reza Arablouei ^{reza.arablouei@csiro.au" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Non-convex optimization ; Perturbed compressive sensing ; Proximal-gradient method ; Rayleigh quotient ; Sparse reconstruction ; Sparse total least-squares
• 刊名：Signal Processing
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：130
• 期：Complete
• 页码：57-63
• 全文大小：836 K

文摘

We consider the problem of finding a sparse solution for an underdetermined linear system of equations when the known parameters on both sides of the system are subject to perturbation. This problem is particularly relevant to reconstruction in fully-perturbed compressive-sensing setups where both the projected measurements of an unknown sparse vector and the knowledge of the associated projection matrix are perturbed due to noise, error, mismatch, etc. We propose a new iterative algorithm for tackling this problem. The proposed algorithm utilizes the proximal-gradient method to find a sparse total least-squares solution by minimizing an l₁${\mathcal{l}}_1$-regularized Rayleigh-quotient cost function. We determine the step-size of the algorithm at each iteration using an adaptive rule accompanied by backtracking line search to improve the algorithm’s convergence speed and preserve its stability. The proposed algorithm is considerably faster than a popular previously-proposed algorithm, which employs the alternating-direction method and coordinate-descent iterations, as it requires significantly fewer computations to deliver the same accuracy. We demonstrate the effectiveness of the proposed algorithm via simulation results.