Systems Engineering and Electronics ›› 2026, Vol. 48 ›› Issue (1): 1-11.doi: 10.12305/j.issn.1001-506X.2026.01.01

• Electronic Technology • Previous Articles     Next Articles

Counter-anti-radiation method method using distributed radiation source blinking decoy

Zhikang LIN(), Jialei LIU, Jiazhi MA, Longfei SHI, Jinbao XU   

  1. College of Electronic Science and Technology,National University of Defense Technology,Changsha 410073,China
  • Received:2024-11-13 Online:2026-01-25 Published:2026-02-11
  • Contact: Jialei LIU E-mail:z_k.kang@nudt.edu.cn

Abstract:

Addressing the issue of how to effectively decoy and protect ground radar through distributed radiation sources coordination when multiple anti-radiation unmanned aerial vehicles (ARUAV) attack simultaneously, a distributed radiation sources blinking decoy method for dual-ARUAV strikes is proposed. The aim is to control the blinking radiation of radiation sources in a distributed stationing manner at a long distance to affect the passive angle measurement of ARUAV, thereby altering its trajectory and ultimately decoying it to land outside the safe radius of the radar radiation sources at the end. This method first analyzes the principle of angle measurement deception for ARUAV using intra-pulse combined signals formed by signal delay control, and designs an ARUAV motion model. Then, a four-dimensional Q-table deep Q-learning framework is established, and a reward function is established based on radar safety distance conditions. The ARUAV position and velocity in a certain airspace are used as inputs for reinforcement learning model training. The simulation results show that the decoy distance of the proposed method is at least 515.91 m, which is better than that of the traditional fixed radiation decoy method, and the decoy distance is at least 68.59% higher than that of the terminal decoy method of fixed radiation under the same station distribution conditions.

Key words: anti-radiation unmanned aerial vehicle (ARUAV), distributed radiation source, reinforcement learning, deep Q-learning (DQL)

CLC Number: 

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