Fast Reconstruction Algorithm for Perturbed Compressive Sensing Based on Total Least-Squares and Proximal Splitting

TL;DR

This paper introduces a fast iterative algorithm for solving perturbed compressive sensing problems, leveraging total least-squares and proximal splitting to efficiently recover sparse signals under measurement and model perturbations.

Contribution

A novel algorithm combining proximal-gradient and adaptive step-size rules for faster sparse recovery in perturbed compressive sensing scenarios.

Findings

Significantly reduces computation time compared to existing methods.

Achieves accurate sparse recovery under measurement and model perturbations.

Demonstrates robustness and efficiency through simulation results.

Abstract

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-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.