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

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.

• Publication year

  2014

• Venue

  IEEE Transactions on Signal Processing

• Publication date

  2014-11-19

• Fields of study

  Mathematics, Computer Science, Engineering

• Identifiers
  DOI 10.1109/TSP.2015.2463259arXiv 1411.5187
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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