Multi‐agent multi‐dimensional joint optimisation of jamming decision‐making against multi‐functional radar

The authors propose a multi‐agent, multi‐dimensional joint optimisation decision‐making strategy to tackle the challenges jammers encounter when interfering with multi‐functional radars, which possess advanced anti‐interference capabilities. The strategy is divided into optimising the interference style and three parameters: timing, power, and duration, to enhance interference effectiveness. Furthermore, state value reuse is combined with the duelling double deep Q‐learning and multi‐agent deep deterministic policy gradient algorithms to effectively manage the multi‐dimensional optimisation of the interference strategy. Simulation results demonstrate that the proposed algorithm outperforms Ant‐QL, heuristic accelerated Q‐learning, and asynchronous advantage actor‐critic algorithms in terms of convergence speed and stability. It identifies a more efficient strategy for transitioning the radar from its initial state to the state with the lowest threat level to the jammer, achieving a 24% improvement in convergence speed. Additionally, the performance curve exhibits better stability.