A new model for DOA estimation and its solution by multi-target intermittent particle swarm optimization
Currently, the widely used methods for direction of arrival (DOA) estimation were constructed based on the subspace, such as Multiple Signal Classification (MUSIC) and Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT), which required that the number of sources is known before...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-04-01
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| Series: | Systems Science & Control Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/21642583.2020.1836525 |
| _version_ | 1818943251647823872 |
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| author | Lizhi Cui Peichao Zhao Xinwei Li Bingfeng Li Keping Wang Xuhui Bu Shumin Fei Yi Yang |
| author_facet | Lizhi Cui Peichao Zhao Xinwei Li Bingfeng Li Keping Wang Xuhui Bu Shumin Fei Yi Yang |
| author_sort | Lizhi Cui |
| collection | DOAJ |
| description | Currently, the widely used methods for direction of arrival (DOA) estimation were constructed based on the subspace, such as Multiple Signal Classification (MUSIC) and Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT), which required that the number of sources is known beforehand. In this paper, a new method based on the Vector Error Model (VEM) for estimating the DOAs was proposed, which do not need the sources number in advance. The comparison of the performance between the VEM and the MUSIC model for DOA problem was given to demonstrate the effectiveness of our method. The algorithm of multi-target intermittent particle swarm optimization (MIPSO) was adopted to solve the VEM, and the performance of the VEM-MIPSO method was analysed through simulations for a uniform linear array and an L-shaped array respectively. The results showed that: (1) the VEM was an effective model to solve the DOA estimation without prior knowledge of the sources number; (2) the MIPSO was an efficient algorithm to solve the DOA estimation with high precision. |
| first_indexed | 2024-12-20T07:24:22Z |
| format | Article |
| id | doaj.art-5c618106c7004f9685517ca3c0574ee9 |
| institution | Directory Open Access Journal |
| issn | 2164-2583 |
| language | English |
| last_indexed | 2024-12-20T07:24:22Z |
| publishDate | 2021-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj.art-5c618106c7004f9685517ca3c0574ee92022-12-21T19:48:35ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832021-04-019S1879510.1080/21642583.2020.18365251836525A new model for DOA estimation and its solution by multi-target intermittent particle swarm optimizationLizhi Cui0Peichao Zhao1Xinwei Li2Bingfeng Li3Keping Wang4Xuhui Bu5Shumin Fei6Yi Yang7School of Electrical Engineering and Automation, Henan Polytechnic UniversitySchool of Electrical Engineering and Automation, Henan Polytechnic UniversitySchool of Electrical Engineering and Automation, Henan Polytechnic UniversitySchool of Electrical Engineering and Automation, Henan Polytechnic UniversitySchool of Electrical Engineering and Automation, Henan Polytechnic UniversitySchool of Electrical Engineering and Automation, Henan Polytechnic UniversitySchool of Electrical Engineering and Automation, Henan Polytechnic UniversitySchool of Electrical Engineering and Automation, Henan Polytechnic UniversityCurrently, the widely used methods for direction of arrival (DOA) estimation were constructed based on the subspace, such as Multiple Signal Classification (MUSIC) and Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT), which required that the number of sources is known beforehand. In this paper, a new method based on the Vector Error Model (VEM) for estimating the DOAs was proposed, which do not need the sources number in advance. The comparison of the performance between the VEM and the MUSIC model for DOA problem was given to demonstrate the effectiveness of our method. The algorithm of multi-target intermittent particle swarm optimization (MIPSO) was adopted to solve the VEM, and the performance of the VEM-MIPSO method was analysed through simulations for a uniform linear array and an L-shaped array respectively. The results showed that: (1) the VEM was an effective model to solve the DOA estimation without prior knowledge of the sources number; (2) the MIPSO was an efficient algorithm to solve the DOA estimation with high precision.http://dx.doi.org/10.1080/21642583.2020.1836525direction of arrivaluniform linear arrayvector error modelmulti-target intermittent particle swarm optimization |
| spellingShingle | Lizhi Cui Peichao Zhao Xinwei Li Bingfeng Li Keping Wang Xuhui Bu Shumin Fei Yi Yang A new model for DOA estimation and its solution by multi-target intermittent particle swarm optimization Systems Science & Control Engineering direction of arrival uniform linear array vector error model multi-target intermittent particle swarm optimization |
| title | A new model for DOA estimation and its solution by multi-target intermittent particle swarm optimization |
| title_full | A new model for DOA estimation and its solution by multi-target intermittent particle swarm optimization |
| title_fullStr | A new model for DOA estimation and its solution by multi-target intermittent particle swarm optimization |
| title_full_unstemmed | A new model for DOA estimation and its solution by multi-target intermittent particle swarm optimization |
| title_short | A new model for DOA estimation and its solution by multi-target intermittent particle swarm optimization |
| title_sort | new model for doa estimation and its solution by multi target intermittent particle swarm optimization |
| topic | direction of arrival uniform linear array vector error model multi-target intermittent particle swarm optimization |
| url | http://dx.doi.org/10.1080/21642583.2020.1836525 |
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