Statistical Recovery of Simultaneously Sparse Time-Varying Signals from Multiple Measurement Vectors

Jun Won Choi, Byonghyo Shim

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

In this paper, we propose a new sparse signal recovery algorithm, referred to as sparse Kalman tree search (sKTS), that provides a robust reconstruction of the sparse vector when the sequence of correlated observation vectors are available. The proposed sKTS algorithm builds on expectation-maximization (EM) algorithm and consists of two main operations: 1) Kalman smoothing to obtain the a posteriori statistics of the source signal vectors and 2) greedy tree search to estimate the support of the signal vectors. Through numerical experiments, we demonstrate that the proposed sKTS algorithm is effective in recovering the sparse signals and performs close to the Oracle (genie-based) Kalman estimator.

Original languageEnglish
Article number7174568
Pages (from-to)6136-6148
Number of pages13
JournalIEEE Transactions on Signal Processing
Volume63
Issue number22
DOIs
Publication statusPublished - 2015 Nov 15
Externally publishedYes

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Recovery
Statistics
Experiments

Keywords

  • Compressed sensing
  • expectation-maximization (EM) algorithm
  • maximum likelihood estimation
  • multiple measurement vector
  • simultaneously sparse signal

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing

Cite this

Statistical Recovery of Simultaneously Sparse Time-Varying Signals from Multiple Measurement Vectors. / Choi, Jun Won; Shim, Byonghyo.

In: IEEE Transactions on Signal Processing, Vol. 63, No. 22, 7174568, 15.11.2015, p. 6136-6148.

Research output: Contribution to journalArticle

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