UAV penetration mission path planning based on improved holonic particle swarm optimization

doi:10.23919/JSEE.2022.000132

Abstract

Abstract:

To meet the requirements of safety, concealment, and timeliness of trajectory planning during the unmanned aerial vehicle (UAV) penetration process, a three-dimensional path planning algorithm is proposed based on improved holonic particle swarm optimization (IHPSO). Firstly, the requirements of terrain threat, radar detection, and penetration time in the process of UAV penetration are quantified. Regarding radar threats, a radar echo analysis method based on radar cross section (RCS) and the spatial situation is proposed to quantify the concealment of UAV penetration. Then the structure-particle swarm optimization (PSO) algorithm is improved from three aspects. First, the conversion ability of the search strategy is enhanced by using the system clustering method and the information entropy grouping strategy instead of random grouping and constructing the state switching conditions based on the fitness function. Second, the unclear setting of iteration numbers is addressed by using particle spacing to create the termination condition of the algorithm. Finally, the trajectory is optimized to meet the intended requirements by building a predictive control model and using the IHPSO for simulation verification. Numerical examples show the superiority of the proposed method over the existing PSO methods.

Key words: path planning, network radar, holonic structure, particle swarm algorithm (PSO), predictive control model

Jing LUO, Qianchao LIANG, Hao LI. UAV penetration mission path planning based on improved holonic particle swarm optimization[J]. Journal of Systems Engineering and Electronics, 2023, 34(1): 197-213.

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Scheme	First stage extensive search	Second stage accurate search
Scheme 1	System clustering method	System clustering method
Scheme 2	Information entropy method	Information entropy method
Scheme 3	System clustering method	Information entropy method
Scheme 4	Information entropy method	System clustering method

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