This invention provides a networked
radar decision-making method based on an improved MADDPG, which balances resolution and anti-interference performance, and relates to the field of
radar anti-interference technology. First, an adversarial
scenario is constructed between the networked
radar and a multi-point
broadband frequency-sweeping jammer,
signal distribution is analyzed, and overlap coefficients are established. Second, the radar cooperative scheduling process is modeled as a multi-agent
partially observable Markov decision process, constructing a composite reward function that balances detection resolution and anti-interference performance. Then, a multi-agent deep deterministic policy gradient
algorithm is used for network training, and a self-attention mechanism is introduced in the value evaluation to extract mutual interference topology features, obtaining the
frequency agility strategy for each radar. This invention overcomes the limitations of traditional discrete frequency hopping, enabling the radar to adaptively utilize fragmented spectrum to elastically expand bandwidth under conditions of no online communication, effectively achieving a comprehensive improvement in high-precision detection and high anti-interference
survivability.