• CONTROL THEORY AND APPLICATION •

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

Jing LUO1,2, Qianchao LIANG1, Hao LI2,*()

1. 1 College of Power Engineering, Naval University of Engineering, Wuhan 430033, China 地址:湖北省武汉市硚口区解放大道717号
2 Department of Intelligence, Air Force Early Warning Academy, Wuhan 430019, China
• Received:2021-02-18 Accepted:2022-07-15 Online:2023-02-18 Published:2023-03-03
• Contact: Hao LI E-mail:afeu_li@163.com
• About author:
LUO Jing was born in 1984. She received her B.E. degree of electronic information, and M.E. degree of information and communication engineering from Wuhan University of Technology, Wuhan, China. She has been pursuing her Ph.D. degree at Naval Engineering University since 2017. Her current research interests are swarm intelligence, intelligence system and signal processing. E-mail: 94523685@qq.com

LIANG Qianchao was born in 1961. He received his M.E. degree of power engineering from Naval Engineering University, Wuhan, China. And completed his Ph.D. degree in power engineering and engineering thermal physics from Huazhong University of Science and Technology in 2004, Wuhan, China. His current research interests are power engineering and simulation and optimization of power mechanical system. E-mail: lqc163cc@163.com

LI Hao was born in 1981. He received his Ph.D. degree in electronic science and technology from Air Force Engineering University in 2017, Xi’an, China. His current research interests are swarm intelligence, UAV swarm, intelligence systems and signal processing. E-mail: afeu_li@163.com
• Supported by:
This work was supported by the National Natural Science Foundation of China (61502522) and Hubei Provincial Natural Science Foundation (2019CFC897)

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.