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  1. 学術雑誌掲載済論文
  2. 洋雑誌

A Novel Modification of PSO Algorithm for SML Estimation of DOA

https://kitami-it.repo.nii.ac.jp/records/8552
e6a707bb-a1ac-4a17-ad14-00f72ffeba34
名前 / ファイル ライセンス アクション
sensors-16-02188.pdf sensors-16-02188 (348.3 kB)
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Item type 学術雑誌論文 / Journal Article(1)
公開日 2017-12-14
タイトル
言語 en
タイトル A Novel Modification of PSO Algorithm for SML Estimation of DOA
言語
言語 eng
資源タイプ
資源 http://purl.org/coar/resource_type/c_6501
タイプ journal article
アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
著者 Chen, Haihua

× Chen, Haihua

WEKO 89661

en Chen, Haihua

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Li, Shibao

× Li, Shibao

WEKO 89662

en Li, Shibao

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Liu, Jianhang

× Liu, Jianhang

WEKO 89663

en Liu, Jianhang

Search repository
Liu, Fen

× Liu, Fen

WEKO 89664

en Liu, Fen

Search repository
Suzuki, Masakiyo

× Suzuki, Masakiyo

WEKO 89665

en Suzuki, Masakiyo

Search repository
著者別名
姓名
姓名 鈴木, 正清
言語 ja
抄録
内容記述タイプ Abstract
内容記述 This paper addresses the issue of reducing the computational complexity of Stochastic Maximum Likelihood (SML) estimation of Direction-of-Arrival (DOA). The SML algorithm is well-known for its high accuracy of DOA estimation in sensor array signal processing. However, its computational complexity is very high because the estimation of SML criteria is a multi-dimensional non-linear optimization problem. As a result, it is hard to apply the SML algorithm to real systems. The Particle Swarm Optimization (PSO) algorithm is considered as a rather efficient method for multi-dimensional non-linear optimization problems in DOA estimation. However, the conventional PSO algorithm suffers two defects, namely, too many particles and too many iteration times. Therefore, the computational complexity of SML estimation using conventional PSO algorithm is still a little high. To overcome these two defects and to reduce computational complexity further, this paper proposes a novel modification of the conventional PSO algorithm for SML estimation and we call it Joint-PSO algorithm. The core idea of the modification lies in that it uses the solution of Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) and stochastic Cramer-Rao bound (CRB) to determine a novel initialization space. Since this initialization space is already close to the solution of SML, fewer particles and fewer iteration times are needed. As a result, the computational complexity can be greatly reduced. In simulation, we compare the proposed algorithm with the conventional PSO algorithm, the classic Altering Minimization (AM) algorithm and Genetic algorithm (GA). Simulation results show that our proposed algorithm is one of the most efficient solving algorithms and it shows great potential for the application of SML in real systems.
書誌情報 en : Sensors

巻 16, 号 12, p. 2188, 発行日 2016
ISSN
収録物識別子タイプ ISSN
収録物識別子 1424-8220
DOI
関連識別子
識別子タイプ DOI
関連識別子 https://doi.org/10.3390/s16122188
権利
権利情報 c2016 by the authors; licensee MDPI, Basel, Switzerland.
出版者
出版者 MDPI
著者版フラグ
値 publisher
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
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