Intelligent interference decision algorithm with prior knowledge embedded LSTM-PPO model

Focusing on the issues of low efficiency and effectiveness in decision-making as well as the instability of traditional reinforcement learning model-based multi-function radar (MFR) jamming decision algorithms, a prior knowledge embedded long short-term memory (LSTM) network-proximal policy optimization (PPO) model based intelligent interference decision algorithm was developed. Firstly, the MFR interference decision problem was regarded as a Markov decision process (MDP). Furthermore, by incorporating prior knowledge associated with the interference domain into the reward function of the PPO model using revenue shaping theory, a reshaped reward function was obtained to guide agent converge quickly so as to improve decision-making efficiency. Besides, leveraging LSTM’s excellent temporal feature extraction ability enables capturing dynamic characteristics of echo data effectively to describe radar working states. Finally, these extracted dynamic features were inputted into the PPO model. With guidance from embedded prior knowledge, an effective interference decision can be achieved rapidly. Simulation results demonstrate that compared to traditional reinforcement learning model based interference decision algorithms, higher efficiency and effectiveness in decision-making can be attained via the proposed algorithms and the MFR interference decision can be efficiently and robustly achieved.