Low-Complexity DOA Estimation via OMP and Majorization-Minimization
Traditional sparse representation algorithms for direction-of-arrival (DOA) estimation always discrete successive azimuths domain and assume the DOAs lie in prior discretized spatial grid. However, discretization incurs errors and leads to poor performance in practice owning to that there always exi...
Uloženo v:
| Vydáno v: | 2018 IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP) s. 18 - 19 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
01.08.2018
|
| Témata: | |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Traditional sparse representation algorithms for direction-of-arrival (DOA) estimation always discrete successive azimuths domain and assume the DOAs lie in prior discretized spatial grid. However, discretization incurs errors and leads to poor performance in practice owning to that there always exist mismatches between the discrete azimuths and the true continuous DOAs. Several efforts have been worked to resolve grid mismatches issue, but these techniques involve serious computational burden. In this paper, a low-complexity DOA estimation method is proposed, which firstly efficiently shrinks dimension of dictionary utilizing Orthogonal Matching Pursuit (OMP), then a iterative refine algorithm is developed by Majorization-Minimization (MM) method. Numerical results show that the proposed algorithm achieves superior performance for handing DOA estimation with low-complexity as well as high accuracy. |
|---|---|
| AbstractList | Traditional sparse representation algorithms for direction-of-arrival (DOA) estimation always discrete successive azimuths domain and assume the DOAs lie in prior discretized spatial grid. However, discretization incurs errors and leads to poor performance in practice owning to that there always exist mismatches between the discrete azimuths and the true continuous DOAs. Several efforts have been worked to resolve grid mismatches issue, but these techniques involve serious computational burden. In this paper, a low-complexity DOA estimation method is proposed, which firstly efficiently shrinks dimension of dictionary utilizing Orthogonal Matching Pursuit (OMP), then a iterative refine algorithm is developed by Majorization-Minimization (MM) method. Numerical results show that the proposed algorithm achieves superior performance for handing DOA estimation with low-complexity as well as high accuracy. |
| Author | Yuan, Yuqi Jiang, Tao Li, Yingsong Zhang, Xiaowei |
| Author_xml | – sequence: 1 givenname: Xiaowei surname: Zhang fullname: Zhang, Xiaowei organization: College of Information and Communication Engineering, Harbin Engineering University, Harbin, Heilongjiang, 150001, China – sequence: 2 givenname: Yingsong surname: Li fullname: Li, Yingsong organization: College of Information and Communication Engineering, Harbin Engineering University, Harbin, Heilongjiang, 150001, China – sequence: 3 givenname: Yuqi surname: Yuan fullname: Yuan, Yuqi organization: College of Information and Communication Engineering, Harbin Engineering University, Harbin, Heilongjiang, 150001, China – sequence: 4 givenname: Tao surname: Jiang fullname: Jiang, Tao organization: College of Information and Communication Engineering, Harbin Engineering University, Harbin, Heilongjiang, 150001, China – sequence: 5 givenname: Yuqi surname: Yuan fullname: Yuan, Yuqi organization: College of Automation, Harbin Engineering University, Harbin, China |
| BookMark | eNotj9FKwzAYhSPohc69gN7kBVKTJU3-Xpa4qdDSXux-pEkKkTUZXVHn01tmrw4fHxzOeUC3MUWP0BOjGWO0eClbXbbZhjLIIOfAJNygdaGAzSBzKQDuka7SN9FpOB39T5gu-LUp8fY8hcFMIUX8FQxu6hab6HBtPtMYfq-C1CGGYYFHdNeb49mvl1yh_W671--kat4-dFmRUNCJWNtbRW3unJKGUa6kE7xTUAjhOqWAG28Fd730eWfFprey4-Bm77jl0lO-Qs__tcF7fziN88bxclie8T_VaUga |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/APCAP.2018.8538168 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE/IET Electronic Library (IEL) (UW System Shared) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| EISBN | 9781538656488 1538656485 |
| EndPage | 19 |
| ExternalDocumentID | 8538168 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IL CBEJK RIE RIL |
| ID | FETCH-LOGICAL-i90t-ccfc70c5dd76a10376d43b78944db7783aec43df6e5bc42fc6b38d789d3c36e03 |
| IEDL.DBID | RIE |
| IngestDate | Thu Jun 29 18:38:57 EDT 2023 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i90t-ccfc70c5dd76a10376d43b78944db7783aec43df6e5bc42fc6b38d789d3c36e03 |
| PageCount | 2 |
| ParticipantIDs | ieee_primary_8538168 |
| PublicationCentury | 2000 |
| PublicationDate | 2018-Aug. |
| PublicationDateYYYYMMDD | 2018-08-01 |
| PublicationDate_xml | – month: 08 year: 2018 text: 2018-Aug. |
| PublicationDecade | 2010 |
| PublicationTitle | 2018 IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP) |
| PublicationTitleAbbrev | APCAP |
| PublicationYear | 2018 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| Score | 1.6910629 |
| Snippet | Traditional sparse representation algorithms for direction-of-arrival (DOA) estimation always discrete successive azimuths domain and assume the DOAs lie in... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 18 |
| SubjectTerms | Array signal processing Azimuth Direction-of-arrival estimation DOA estimation Estimation Iterative methods Majorization-Minimization (MM) Matching pursuit algorithms Orthogonal Matching Pursuit (OMP) Signal processing algorithms sparse representation |
| Title | Low-Complexity DOA Estimation via OMP and Majorization-Minimization |
| URI | https://ieeexplore.ieee.org/document/8538168 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlZ1LawIxEICDSg89tUVL3-TQY6OryeZxFFF6ULsHD94kmWRhC90tdlX675vsbi2FXnrLC8IkhMkk880g9MiNovHQCaLVSBBmh4Zo560UyyKTai5HBqqQ-XOxXMr1WiUt9HRkYZxzlfOZ64di9ZdvC9iFp7KBVy0hTUQbtYXgNav1zcFEajBOJuMkOGvJfjPwV8aUSmHMzv431Tnq_ZB3ODnqlAvUcnkXTebFgYSDG4JXlp_Y23N46o9mTR3ifabxyyLBOrd4oV-LbYNWkkWWZ29NpYdWs-lq8kya5AckU1FJAFIQEcTWCq4Dy8cto0ZIxZg1QkiqHTBqU-5iA2yUAjdUWt9vKVDuInqJOnmRuyuEAaTxFwklrJEsBlCGDZk2zkovobe-rlE3yL95r8NbbBrRb_5uvkWnYYlrH7g71Cm3O3ePTmBfZh_bh2pPvgAJ3JBR |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlZ3NS8MwFMDDnIKeVDbx2xw8mq1r0iY5jrExsa097LDbyFehgq3MbuJ_b9LWieDFW74gvITw8pL3ew-A-1ByHIwMRYL7FBE9kkgYa6Vo4slMhMyXqg6ZH9EkYcslTzvgYcfCGGNq5zMzcMX6L1-XauOeyoZWtbg0EXtgPyDE9xpa65uE8fhwnE7GqXPXYoN26K-cKbXKmB3_b7IT0P9h72C60yqnoGOKHphE5QdyR9eFr6w-obXo4NQezoY7hNtcwOc4haLQMBYv5bqFK1GcF_lrW-mDxWy6mMxRm_4A5dyrkFKZop4KtKahcDRfqAmWlHFCtKSUYWEUwToLTSAV8TMVSsy07ddY4dB4-Ax0i7Iw5wAqxaS9SnCqJSOBUlySERHSaGYltPbXBeg5-VdvTYCLVSv65d_Nd-BwvoijVfSYPF2BI7fcjUfcNehW6425AQdqW-Xv69t6f74AuxeTmA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2018+IEEE+Asia-Pacific+Conference+on+Antennas+and+Propagation+%28APCAP%29&rft.atitle=Low-Complexity+DOA+Estimation+via+OMP+and+Majorization-Minimization&rft.au=Zhang%2C+Xiaowei&rft.au=Li%2C+Yingsong&rft.au=Yuan%2C+Yuqi&rft.au=Jiang%2C+Tao&rft.date=2018-08-01&rft.pub=IEEE&rft.spage=18&rft.epage=19&rft_id=info:doi/10.1109%2FAPCAP.2018.8538168&rft.externalDocID=8538168 |