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Cooperative localisation of UAV swarm based on adaptive SA-PSO algorithm

Published online by Cambridge University Press:  19 May 2022

W. Ma Open the ORCID record for W. Ma [Opens in a new window] ,
Y. Fang Open the ORCID record for Y. Fang [Opens in a new window] ,
W. Fu ,
S. Liu Open the ORCID record for S. Liu [Opens in a new window]  and
E. Guo
W. Ma
Affiliation:
School of Automation, Northwestern Polytechnical University, Xi’an, China
Y. Fang*
Affiliation:
Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, China
W. Fu
Affiliation:
Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, China
S. Liu
Affiliation:
Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, China
E. Guo
Affiliation:
School of Computer Science, Xi ’an University of Posts and Telecommunications, Xi’an, China
*
*Corresponding author. Email: ywfang@nwpu.edu.cn
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Abstract

In this paper, to address the cooperative localisation of a heterogeneous UAV swarm in the GNSS-denied environment, an adaptive simulated annealing-particle swarm optimisation (SA-PSO) cooperative localisation algorithm is proposed. Firstly, the forming principle of the communication and measurement framework is investigated in light of a heterogeneous UAV swarm composition. Secondly, a reasonably cooperative localisation function is established based on the proposed forming principle, which can minimise the relative localisation error with limited available information. Then, an adaptive weight principle is incorporated into the particle swarm optimisation (PSO) for better performance. Furthermore, in order to overcome the drawbacks of PSO algorithm easily falling into the local extreme point, an adaptive SA-PSO algorithm is improved to promote the convergence speed of cooperative localisation. Finally, comparative simulations are performed among the adaptive SA-PSO, adaptive PSO, and PSO algorithms to demonstrate the feasibility and superiority of the proposed adaptive SA-PSO algorithm. Simulation results show that the proposed algorithm has better performance in convergence speed, and the cooperative localisation precision can be guaranteed.

Keywords

Cooperative localisationHeterogeneous UAV swarmForming principleAdaptiveSimulated annealing algorithmParticle swarm optimisation

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 127 , Issue 1307 , January 2023 , pp. 57 - 75
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Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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