An Optimal Geometry Configuration Algorithm of Hybrid Semi-Passive Location System Based on Mayfly Optimization Algorithm
In view of the demand of location awareness in a special complex environment, for an unmanned aerial vehicle (UAV) airborne multi base-station semi-passive positioning system, the hybrid positioning solutions and optimized site layout in the positioning system can effectively improve the positioning...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/22/7484 |
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| author | Aihua Hu Zhongliang Deng Hui Yang Yao Zhang Yuhui Gao Di Zhao |
| author_facet | Aihua Hu Zhongliang Deng Hui Yang Yao Zhang Yuhui Gao Di Zhao |
| author_sort | Aihua Hu |
| collection | DOAJ |
| description | In view of the demand of location awareness in a special complex environment, for an unmanned aerial vehicle (UAV) airborne multi base-station semi-passive positioning system, the hybrid positioning solutions and optimized site layout in the positioning system can effectively improve the positioning accuracy for a specific region. In this paper, the geometric dilution of precision (GDOP) formula of a time difference of arrival (TDOA) and angles of arrival (AOA) hybrid location algorithm is deduced. Mayfly optimization algorithm (MOA) which is a new swarm intelligence optimization algorithm is introduced, and a method to find the optimal station of the UAV airborne multiple base station’s semi-passive positioning system using MOA is proposed. The simulation and analysis of the optimization of the different number of base stations, compared with other station layout methods, such as particle swarm optimization (PSO), genetic algorithm (GA), and artificial bee colony (ABC) algorithm. MOA is less likely to fall into local optimum, and the error of regional target positioning is reduced. By simulating the deployment of four base stations and five base stations in various situations, MOA can achieve a better deployment effect. The dynamic station configuration capability of the multi-station semi-passive positioning system has been improved with the UAV. |
| first_indexed | 2024-03-10T05:05:36Z |
| format | Article |
| id | doaj.art-fae6cae395844181bf52579ba2b12df8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:05:36Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fae6cae395844181bf52579ba2b12df82023-11-23T01:24:04ZengMDPI AGSensors1424-82202021-11-012122748410.3390/s21227484An Optimal Geometry Configuration Algorithm of Hybrid Semi-Passive Location System Based on Mayfly Optimization AlgorithmAihua Hu0Zhongliang Deng1Hui Yang2Yao Zhang3Yuhui Gao4Di Zhao5School of Electronic Engineering, Beijing University of Posts and Communications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Communications, Beijing 100876, ChinaAstronaut Research and Training Center, Beijing 100094, ChinaSchool of Electronic Engineering, Beijing University of Posts and Communications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Communications, Beijing 100876, ChinaSchool of Electronic Engineering, Beijing University of Posts and Communications, Beijing 100876, ChinaIn view of the demand of location awareness in a special complex environment, for an unmanned aerial vehicle (UAV) airborne multi base-station semi-passive positioning system, the hybrid positioning solutions and optimized site layout in the positioning system can effectively improve the positioning accuracy for a specific region. In this paper, the geometric dilution of precision (GDOP) formula of a time difference of arrival (TDOA) and angles of arrival (AOA) hybrid location algorithm is deduced. Mayfly optimization algorithm (MOA) which is a new swarm intelligence optimization algorithm is introduced, and a method to find the optimal station of the UAV airborne multiple base station’s semi-passive positioning system using MOA is proposed. The simulation and analysis of the optimization of the different number of base stations, compared with other station layout methods, such as particle swarm optimization (PSO), genetic algorithm (GA), and artificial bee colony (ABC) algorithm. MOA is less likely to fall into local optimum, and the error of regional target positioning is reduced. By simulating the deployment of four base stations and five base stations in various situations, MOA can achieve a better deployment effect. The dynamic station configuration capability of the multi-station semi-passive positioning system has been improved with the UAV.https://www.mdpi.com/1424-8220/21/22/7484optimal geometry configurationsemi-passive locationGDOPMOATDOA&AOAUAV |
| spellingShingle | Aihua Hu Zhongliang Deng Hui Yang Yao Zhang Yuhui Gao Di Zhao An Optimal Geometry Configuration Algorithm of Hybrid Semi-Passive Location System Based on Mayfly Optimization Algorithm Sensors optimal geometry configuration semi-passive location GDOP MOA TDOA&AOA UAV |
| title | An Optimal Geometry Configuration Algorithm of Hybrid Semi-Passive Location System Based on Mayfly Optimization Algorithm |
| title_full | An Optimal Geometry Configuration Algorithm of Hybrid Semi-Passive Location System Based on Mayfly Optimization Algorithm |
| title_fullStr | An Optimal Geometry Configuration Algorithm of Hybrid Semi-Passive Location System Based on Mayfly Optimization Algorithm |
| title_full_unstemmed | An Optimal Geometry Configuration Algorithm of Hybrid Semi-Passive Location System Based on Mayfly Optimization Algorithm |
| title_short | An Optimal Geometry Configuration Algorithm of Hybrid Semi-Passive Location System Based on Mayfly Optimization Algorithm |
| title_sort | optimal geometry configuration algorithm of hybrid semi passive location system based on mayfly optimization algorithm |
| topic | optimal geometry configuration semi-passive location GDOP MOA TDOA&AOA UAV |
| url | https://www.mdpi.com/1424-8220/21/22/7484 |
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