Learning and planning in partially observable environments without prior domain knowledge

Current electronic warfare jammers and radar countermeasures are characterized by dynamism and uncertainty. This paper focuses on a decision-making framework of radar anti-jamming countermeasures. The characteristics and implementation process of radar intelligent anti-jamming systems are analyzed, and a scheduling method for radar anti-jamming action based on the Partially Observable Markov Process (POMDP) is proposed. The sample-based belief distribution is used to reflect the radar's cognition of the environment and describes the uncertainty of the recognition of jamming patterns in the belief state space. The belief state of jamming patterns is updated with Bayesian rules. The reward function is used as the evaluation criterion to select the best anti-jamming strategy, so that the radar is in a low threat state as often as possible. Numerical simulation combines the behavioral prior knowledge base of radars and jammers and obtains the behavioral confrontation benefit matrix from the past experience of experts. The radar controls the output according to the POMDP policy, and dynamically performs the best anti-jamming action according to the change of jamming state. The results show that the POMDP anti-jamming policy is better than the conventional policy. The POMDP approach improves the adaptive anti-jamming capability of the radar and can quickly realize the anti-jamming decision to jammers. This work provides some design ideas for the subsequent development of an intelligent radar.

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Learning and planning in partially observable environments without prior domain knowledge

Cited by 2 publications

References 22 publications

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Mastering the Card Game of Jaipur Through Zero-Knowledge Self-Play Reinforcement Learning and Action Masks

Radar Anti-Jamming Countermeasures Intelligent Decision-Making: A Partially Observable Markov Decision Process Approach

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