Abstract
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.
Highlights
Published: 11 November 2021At present, people are increasingly dependent on location services
We propose an optimal geometry configuration algorithm of hybrid location systems based on Mayfly optimization algorithm (MOA), which is for the unmanned aerial vehicles (UAV) airborne multi-base station semi-passive positioning system
geometric dilution of precision (GDOP) is the square root of the trace of Cramer Rao lower bound (CRLB), which is the inverse of Fisher information matrix (FIM)
Summary
People are increasingly dependent on location services. In addition to indoor and outdoor applications, location awareness applications for extreme environments are increasing, such as searching and rescuing after natural disasters, submarine sensor network positioning, etc. Et al proposed the optimal deployment of sensor–emitter geometries for hybrid localization using TDOA and AOA measurements [12]. Et al proposed optimal sensor deployment with hybrid TDOA and FDOA measurements [14]. No one has studied the optimization of the base station layout of the wireless positioning system in the ruin environment. We propose an optimal geometry configuration algorithm of hybrid location systems based on MOA, which is for the UAV airborne multi-base station semi-passive positioning system. The location of a ruined environment is complex, and the hybrid location algorithm of TDOA and AOA deduced the target location. The remainder of this paper is organized as follows: Section 2 gives the formulae of TDOA and AOA hybrid positioning algorithm obtained from one transmitter and n receivers.
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