Estimation of Dynamically Varying Support of Sparse Signals via Sequential Monte-Carlo Method
| Autor: | Jun Won Choi, Sun Hong Lim, Byonghyo Shim, Jin Hyeok Yoo |
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| Rok vydání: | 2020 |
| Předmět: |
Sequence
Computer science Markov process 020206 networking & telecommunications 02 engineering and technology Kalman filter Set (abstract data type) symbols.namesake Compressed sensing Joint probability distribution Signal Processing 0202 electrical engineering electronic engineering information engineering symbols Electrical and Electronic Engineering Particle filter Algorithm Gibbs sampling |
| Zdroj: | IEEE Transactions on Signal Processing. 68:4135-4147 |
| ISSN: | 1941-0476 1053-587X |
| DOI: | 10.1109/tsp.2020.3007962 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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