A Fast and Noise-Robust Algorithm for Joint Sparse Recovery Through Information Transfer

In multiple measurement vector (MMV) problems, L measurement vectors each of which has length M are available for recovering jointly sparse signals that have a common support set of size K. In this paper, a fast and noise-robust greedy algorithm is proposed for joint sparse recovery in MMV problems,...

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Vydané v:IEEE access Ročník 7; s. 37735 - 37748
Hlavný autor: Yu, Nam Yul
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
A posteriori probability
Algorithms
Asymptotic properties
compressed sensing
Greedy algorithms
Indexes
Information transfer
joint sparse recovery
Matched pursuit
Matching
Matching pursuit algorithms
multiple measurement vectors
Multiple signal classification
Noise
Noise robustness
order statistics
Recovery
Reliability
Robustness
ISSN:2169-3536, 2169-3536
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Popis
Shrnutí:In multiple measurement vector (MMV) problems, L measurement vectors each of which has length M are available for recovering jointly sparse signals that have a common support set of size K. In this paper, a fast and noise-robust greedy algorithm is proposed for joint sparse recovery in MMV problems, by exploiting a posteriori probability ratios for every index of sparse input signals. The essence of the algorithm is to transfer the information through iterations, which contributes to the performance improvement of support detection. When L is sufficiently large at M = K +1, we investigate the asymptotic performance of exact support recovery using a Gaussian assumption, power-law approximations, and order statistics, where the techniques are inspired by experimental results. In this case, we also present a sufficient condition on the number of measurements, or M = K + 1 = Ω (log N), for theoretical support recovery guarantee. The theoretical analysis reveals that the proposed algorithm can achieve reliable joint sparse recovery asymptotically at the theoretical limit of M = K + 1 with high probability. By examining the performance for various M, K, and L, simulation results demonstrate that if M is not too small, the proposed algorithm can be reliable, fast, and noise-robust, compared to the conventional ones, such as simultaneous orthogonal matching pursuit (SOMP), subspace augmented MUSIC (SA-MUSIC), and rank-aware order recursive matching pursuit (RA-ORMP).
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2905903