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A RIP-Based Performance Guarantee of Covariance-Assisted Matching Pursuit

Wang, J, Li, G, Rencker, Lucas, Wang, Wenwu and Gu, Y (2018) A RIP-Based Performance Guarantee of Covariance-Assisted Matching Pursuit IEEE Signal Processing Letters, 25 (6). pp. 828-832.

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An OMP-like Covariance-Assisted Matching Pursuit (CAMP) method has recently been proposed. Given a priorknowledge of the covariance and mean of the sparse coefficients, CAMP balances the least squares estimator and the priorknowledge by leveraging the Gauss-Markov theorem. In this letter, we study the performance of CAMP in the framework of restricted isometry property (RIP). It is shown that under some conditions on RIP and the minimum magnitude of the nonzero elements of the sparse signal, CAMP with sparse level K can recover the exact support of the sparse signal from noisy measurements. l2 bounded noise and Gaussian noise are considered in our analysis.We also discuss the extreme conditions of noise (e.g. the noise power is infinite) to simply show the stability of CAMP.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Wang, J
Li, G
Gu, Y
Date : 16 March 2018
DOI : 10.1109/LSP.2018.2816573
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Sparse recovery, Covariance-Assisted Matching Pursuit (CAMP), restricted isometry property (RIP), compressed sensing.
Depositing User : Melanie Hughes
Date Deposited : 13 Mar 2018 15:45
Last Modified : 16 Jan 2019 19:08

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