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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The weapon target allocation (WTA) problem is a crucial issue in anti-missile command decisions. However, the current anti-missile weapon target allocation models ignore the dynamic complexity, cooperation, and uncertainty in the actual combat process, which results in the misclassification and omission of targets. Therefore, we propose a bi-level dynamic anti-missile weapon target allocation model based on rolling horizon optimization and marginal benefit reprogramming to achieve rapid impact on static and dynamic uncertainties in the battlefield environment. Further, we also propose an improved bi-level recursive BBO algorithm based on hybrid migration and variation to perform fast and efficient optimization of the model objective function. A simulation analysis demonstrate that the model is suitable for larger-scale, complex, dynamic anti-missile operations in uncertain environments, while the algorithm achievesbetter solution efficiency and solution time compared with the same type of heuristic algorithm, which meet the requirements of solution accuracy and timeliness. In addition, we obtain better rolling horizon parameters to further optimize its performance.

This paper studies the resource allocation problem when multiple jammers follow the aircraft formation to support ground penetration. A joint optimization allocation method of multi-jammer beam-power based on the improved artificial bee colony (IABC) algorithm is proposed. The air-to-ground “many-to-many” assault of the multi-jammer cooperative suppression jamming model is given. The constant false alarm probability detection model of the networked radar is used to evaluate the suppression effect, and a coordinated control model of multi-jammer jamming beams and emitting power is established. The optimal allocation scheme under different combat scenarios is solved by using the IABC algorithm. The search efficiency of the ABC algorithm is improved by cross mutation operation and the replacement of the worst nectar source, and the search performance of the algorithm is enhanced by the random key encoding. Due to the infeasible solution generated by the special random key encoding method, the feasible adjustment strategy is adopted. By changing the jamming parameters, the effect on the detection probability of the radar network is analyzed. Compared to the GWO, SCA, BBO and ABC algorithms, the jamming resource allocation scheme obtained by the proposed IABC algorithm makes the radar detection probability lower. The IABC algorithm has better global search capability and robustness.