Optimizing Jamming Type Selection and Power Allocation for Countering Multifunctional Radar Network Based on IMAHPPO Algorithm

Multifunctional radar (MFR) can adjust to different working modes based on surrounding environment information, providing a degree of antijamming capability. With the enhancement of information exchange capabilities, technologies such as cooperative detection and guidance have advanced significantly, further strengthening the antijamming capabilities of MFR network. Therefore, this article builds a networked radar (NR) model based on complex network theory and presents the radar countermeasure game problem of joint jamming type selection and power allocation under power constraints. Specifically, this article employs multiagent reinforcement learning algorithms to optimize the selection of jamming types and power allocation for multiple jamming beams, thereby maximizing the long-term jamming effectiveness on NR. To address the hybrid action space decision-making problem in jamming, a serial hierarchical policy network with a shared evaluation network is designed. In addition, according to the jamming task, the state characteristics and total reward are designed reasonably to assist the strategy exploration. Finally, we propose an improved multiagent hybrid proximal policy optimization (IMAHPPO) algorithm for devising jamming strategies and compare its performance with the standard MAHPPO algorithm and the multiagent advantage actor-critic algorithm, demonstrating the effectiveness and adaptability of the proposed approach.