Estimation of Dynamically Varying Support of Sparse Signals via Sequential Monte-Carlo Method

Jin Hyeok Yoo, Sun Hong Lim, Byonghyo Shim, Jun Won Choi

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In this paper, we address the problem of tracking time-varying support of a sparse signal given a sequence of observation vectors. We model the dynamic variation of the support set using the discrete-state Markov process and employ the Rao-Blackwellized sequential Monte Carlo method, which allows for separate tracking of the support set and the amplitude of the unknown signals. Specifically, the samples for the support variables are drawn from their posteriori joint distributions using a Gibbs sampler while the continuous amplitude variables are separately estimated using the Kalman filter. Our numerical evaluation shows that the proposed method achieves significant performance gain over the existing sparse estimation methods.

Original languageEnglish
Article number9139387
Pages (from-to)4135-4147
Number of pages13
JournalIEEE Transactions on Signal Processing
Volume68
DOIs
StatePublished - 2020

Keywords

  • Rao-Blackwellization
  • Sparse recovery algorithm
  • compressed sensing
  • particle filter
  • sequential Monte-Carlo method
  • support recovery

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