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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Autores principales:	Aihua Hu, Zhongliang Deng, Hui Yang, Yao Zhang, Yuhui Gao, Di Zhao
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	optimal geometry configuration semi-passive location GDOP MOA TDOA&AOA UAV Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/fae6cae395844181bf52579ba2b12df8
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spelling	oai:doaj.org-article:fae6cae395844181bf52579ba2b12df82021-11-25T18:56:49ZAn Optimal Geometry Configuration Algorithm of Hybrid Semi-Passive Location System Based on Mayfly Optimization Algorithm10.3390/s212274841424-8220https://doaj.org/article/fae6cae395844181bf52579ba2b12df82021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7484https://doaj.org/toc/1424-8220In 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.Aihua HuZhongliang DengHui YangYao ZhangYuhui GaoDi ZhaoMDPI AGarticleoptimal geometry configurationsemi-passive locationGDOPMOATDOA&AOAUAVChemical technologyTP1-1185ENSensors, Vol 21, Iss 7484, p 7484 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	optimal geometry configuration semi-passive location GDOP MOA TDOA&AOA UAV Chemical technology TP1-1185
spellingShingle	optimal geometry configuration semi-passive location GDOP MOA TDOA&AOA UAV Chemical technology TP1-1185 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
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.
format	article
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
title	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_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_sort	optimal geometry configuration algorithm of hybrid semi-passive location system based on mayfly optimization algorithm
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/fae6cae395844181bf52579ba2b12df8
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An Optimal Geometry Configuration Algorithm of Hybrid Semi-Passive Location System Based on Mayfly Optimization Algorithm

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