Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 최준원 | - |
dc.date.accessioned | 2022-03-31T00:29:36Z | - |
dc.date.available | 2022-03-31T00:29:36Z | - |
dc.date.issued | 2020-07 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON SIGNAL PROCESSING, v. 68, page. 4135-4147 | en_US |
dc.identifier.issn | 1053-587X | - |
dc.identifier.issn | 1941-0476 | - |
dc.identifier.uri | https://ieeexplore.ieee.org/document/9139387 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/169578 | - |
dc.description.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. | en_US |
dc.description.sponsorship | The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Shogo Muramatsu. This work was supported in part by the Institute of Information & Communications Technology Planning& Evaluation (IITP) grant funded by theKorea government (MSIT) (No. 2020-0-01373, Artificial Intelligence Graduate School Program (Hanyang University)) and in part by the MSIT under the ITRC (Information Technology Research Center) support program (IITP-2019-2017-0-01637) supervised by the IITP. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | en_US |
dc.subject | Sparse recovery algorithm | en_US |
dc.subject | compressed sensing | en_US |
dc.subject | particle filter | en_US |
dc.subject | support recovery | en_US |
dc.subject | Rao-Blackwellization | en_US |
dc.subject | sequential Monte-Carlo method | en_US |
dc.title | Estimation of Dynamically Varying Support of Sparse Signals via Sequential Monte-Carlo Method | en_US |
dc.type | Article | en_US |
dc.relation.volume | 68 | - |
dc.identifier.doi | 10.1109/TSP.2020.3007962 | - |
dc.relation.page | 4135-4147 | - |
dc.relation.journal | IEEE TRANSACTIONS ON SIGNAL PROCESSING | - |
dc.contributor.googleauthor | Yoo, Jin Hyeok | - |
dc.contributor.googleauthor | Lim, Sun Hong | - |
dc.contributor.googleauthor | Shim, Byonghyo | - |
dc.contributor.googleauthor | Choi, Jun Won | - |
dc.relation.code | 2020047921 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | SCHOOL OF ELECTRICAL AND BIOMEDICAL ENGINEERING | - |
dc.identifier.pid | junwchoi | - |
dc.identifier.orcid | https://orcid.org/0000-0002-3733-0148 | - |
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