A Low-Complexity GA-WSF Algorithm for Narrow-Band DOA Estimation

This paper proposes a low-complexity estimation algorithm for weighted subspace fitting (WSF) based on the Genetic Algorithm (GA) in the problem of narrow-band direction-of-arrival (DOA) finding. Among various solving techniques for DOA, WSF is one of the highest estimation accuracy algorithms. Howe...

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Main Authors: Haihua Chen, Jialiang Hu, Hui Tian, Shibao Li, Jianhang Liu, Masakiyo Suzuki
Format: Article
Language:English
Published: Wiley 2018-01-01
Series:International Journal of Antennas and Propagation
Online Access:http://dx.doi.org/10.1155/2018/7175653
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author Haihua Chen
Jialiang Hu
Hui Tian
Shibao Li
Jianhang Liu
Masakiyo Suzuki
author_facet Haihua Chen
Jialiang Hu
Hui Tian
Shibao Li
Jianhang Liu
Masakiyo Suzuki
author_sort Haihua Chen
collection DOAJ
description This paper proposes a low-complexity estimation algorithm for weighted subspace fitting (WSF) based on the Genetic Algorithm (GA) in the problem of narrow-band direction-of-arrival (DOA) finding. Among various solving techniques for DOA, WSF is one of the highest estimation accuracy algorithms. However, its criteria is a multimodal nonlinear multivariate optimization problem. As a result, the computational complexity of WSF is very high, which prevents its application to real systems. The Genetic Algorithm (GA) is considered as an effective algorithm for finding the global solution of WSF. However, conventional GA usually needs a big population size to cover the whole searching space and a large number of generations for convergence, which means that the computational complexity is still high. To reduce the computational complexity of WSF, this paper proposes an improved Genetic algorithm. Firstly a hypothesis technique is used for a rough DOA estimation for WSF. Then, a dynamic initialization space is formed around this value with an empirical function. Within this space, a smaller population size and smaller amount of generations are required. Consequently, the computational complexity is reduced. Simulation results show the efficiency of the proposed algorithm in comparison to many existing algorithms.
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institution Kabale University
issn 1687-5869
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language English
publishDate 2018-01-01
publisher Wiley
record_format Article
series International Journal of Antennas and Propagation
spelling doaj-art-52d7da4112bc4caebc4fe33994823d7b2025-02-03T07:24:52ZengWileyInternational Journal of Antennas and Propagation1687-58691687-58772018-01-01201810.1155/2018/71756537175653A Low-Complexity GA-WSF Algorithm for Narrow-Band DOA EstimationHaihua Chen0Jialiang Hu1Hui Tian2Shibao Li3Jianhang Liu4Masakiyo Suzuki5College of Computer and Communication Engineering, China University of Petroleum, Qingdao, Shandong 266580, ChinaCollege of Computer and Communication Engineering, China University of Petroleum, Qingdao, Shandong 266580, ChinaCollege of Computer and Communication Engineering, China University of Petroleum, Qingdao, Shandong 266580, ChinaCollege of Computer and Communication Engineering, China University of Petroleum, Qingdao, Shandong 266580, ChinaCollege of Computer and Communication Engineering, China University of Petroleum, Qingdao, Shandong 266580, ChinaGraduate School of Engineering, Kitami Institute of Technology, Kitami, Hokkaido 090-8507, JapanThis paper proposes a low-complexity estimation algorithm for weighted subspace fitting (WSF) based on the Genetic Algorithm (GA) in the problem of narrow-band direction-of-arrival (DOA) finding. Among various solving techniques for DOA, WSF is one of the highest estimation accuracy algorithms. However, its criteria is a multimodal nonlinear multivariate optimization problem. As a result, the computational complexity of WSF is very high, which prevents its application to real systems. The Genetic Algorithm (GA) is considered as an effective algorithm for finding the global solution of WSF. However, conventional GA usually needs a big population size to cover the whole searching space and a large number of generations for convergence, which means that the computational complexity is still high. To reduce the computational complexity of WSF, this paper proposes an improved Genetic algorithm. Firstly a hypothesis technique is used for a rough DOA estimation for WSF. Then, a dynamic initialization space is formed around this value with an empirical function. Within this space, a smaller population size and smaller amount of generations are required. Consequently, the computational complexity is reduced. Simulation results show the efficiency of the proposed algorithm in comparison to many existing algorithms.http://dx.doi.org/10.1155/2018/7175653
spellingShingle Haihua Chen
Jialiang Hu
Hui Tian
Shibao Li
Jianhang Liu
Masakiyo Suzuki
A Low-Complexity GA-WSF Algorithm for Narrow-Band DOA Estimation
International Journal of Antennas and Propagation
title A Low-Complexity GA-WSF Algorithm for Narrow-Band DOA Estimation
title_full A Low-Complexity GA-WSF Algorithm for Narrow-Band DOA Estimation
title_fullStr A Low-Complexity GA-WSF Algorithm for Narrow-Band DOA Estimation
title_full_unstemmed A Low-Complexity GA-WSF Algorithm for Narrow-Band DOA Estimation
title_short A Low-Complexity GA-WSF Algorithm for Narrow-Band DOA Estimation
title_sort low complexity ga wsf algorithm for narrow band doa estimation
url http://dx.doi.org/10.1155/2018/7175653
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