Title :
Sufficient Conditions on Stable Recovery of Sparse Signals With Partial Support Information
Author :
Xiaohan Yu ; Seung Jun Baek
Author_Institution :
Dept. of Comput. & Commun. Eng., Korea Univ., Seoul, South Korea
Abstract :
In this letter, we study signal reconstruction from compressed sensing measurements. We propose new sufficient conditions for stable recovery when partial support information is available. Weighted l1-minimization is adopted to recover the original signal under three noise models. The proposed approach is to use Ozeki´s inequality and shifting inequality in order to bound the errors in the associated weighted l1 -minimization. Our result offers generalized performance bounds on recovery capturing known support information. Improved sufficient conditions for recovery are derived based on our results, even for the cases where the accuracy of prior support information is arbitrarily low.
Keywords :
minimisation; signal reconstruction; Ozeki inequality; compressed sensing measurements; generalized performance bounds; noise models; partial support information; shifting inequality; signal reconstruction; sparse signal stable recovery; weighted l1-minimization; Compressed sensing; Estimation; Noise; Sensors; Sparse matrices; Standards; Vectors; Compressive sensing (CS); partial support information; weighted $ell_{1}$-minimization;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2013.2254712
