Abstract
This paper investigates an intelligent trajectory planning algorithm for unmanned aerial vehicles (UAVs) that utilizes track deception techniques against radar networks. The main objective of the algorithm is to minimize the flight distance of UAVs while generating a coherent phantom track to deceive radar networks. Firstly, we analyze the geometric coupling relationship among the false target, the UAV, and the radar, deriving the motion control equations for UAV trajectory planning. Subsequently, we mathematically formulate the problem of intelligent trajectory planning for UAVs based on this coupling relationship. The optimization model aims to reduce the flight distance of the UAVs as much as possible, considering the strict dynamic constraints of UAV platforms. Furthermore, by leveraging the pre-designed phantom track and prior knowledge of radar network locations, we employ the particle swarm optimization (PSO) method to solve the resulting optimization problem. Finally, through simulation results, we validate the effectiveness and feasibility of the proposed algorithm.
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References
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Acknowledgments
This work is supported in part by the National Natural Science Foundation of China under Grant 62271247, in part by the Defense Industrial Technology Development Program under Grant JCKY2021210B004, in part by the Fund of Prospective Layout of Scientific Research for NUAA (Nanjing University of Aeronautics and Astronautics), in part by the National Aerospace Science Foundation of China under Grant 20220055052001, and in part by Key Laboratory of Radar Imaging and Microwave Photonics (Nanjing Univ. Aeronaut. Astronaut.), Ministry of Education, Nanjing, China.
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Lin, W., Shi, C., Wang, Z., Zhou, J. (2024). Intelligent Trajectory Planning Algorithm for Unmanned Aerial Vehicles Based on Track Deception Against Radar Networks. In: Qu, Y., Gu, M., Niu, Y., Fu, W. (eds) Proceedings of 3rd 2023 International Conference on Autonomous Unmanned Systems (3rd ICAUS 2023). ICAUS 2023. Lecture Notes in Electrical Engineering, vol 1174. Springer, Singapore. https://doi.org/10.1007/978-981-97-1091-1_6
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DOI: https://doi.org/10.1007/978-981-97-1091-1_6
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