Systems Engineering and Electronics ›› 2026, Vol. 48 ›› Issue (5): 1551-1563.doi: 10.12305/j.issn.1001-506X.2026.05.11

• Sensors and Signal Processing • Previous Articles     Next Articles

Robust trajectory planning algorithm for UAVs swarm facing deceptive jamming networked radar

Wenbin LIN1, Chenguang SHI1,*, Mu YAN2, Fei WANG1, Jianjiang ZHOU1   

  1. 1. Key Laboratory of Radar Imaging and Microwave Photonics,Ministry of Education,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
    2. Laboratory of Electromagnetic Space Cognition and Intelligent Control,Beijing 100191,China
  • Received:2024-07-15 Online:2026-05-27 Published:2026-05-27
  • Contact: Chenguang SHI

Abstract:

To address the issue of poor deception effectiveness caused by errors in radar locations and storage-forwarding devices, research is conducted on robust trajectory planning algorithms for unmanned aerial vehicle (UAV) swarm facing deceptive jamming networked radar. Firstly, the spatial resolution unit of the networked radar and its “homogeneity test” criteria are analyzed to determine the conditions for UAV swarm to implement trajectory deception on networked radar. Secondly, with the constraint of satisfying the flight dynamics limitations of each UAV, and the optimization objective of minimizing the deviation between the actual generated false targets and the preset false targets of the UAV swarm, a robust trajectory planning mathematical model for the UAV swarm oriented deception jamming networked radar is established. On this basis, considering the normal random errors in radar site locations and storage-forwarding devices, the particle swarm optimization algorithm is employed to solve the optimization model. Simulation results indicate that under the condition of satisfying the dynamic constraints of each UAV, the proposed algorithm can improve the success rate of trajectory deception in complex error conditions and achieve robust trajectory deception against networked radar.

Key words: trajectory planning, trajectory deception, error analysis, unmanned aerial vehicle (UAV) swarm, particle swarm optimization (PSO) algorithm, networked radar

CLC Number: 

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