Intelligent Anti-jamming Decision Algorithm of Frequency Hopping System Based on DE-SARSA(TS)
To increase the anti-jamming performance of frequency hopping communication system in complex electromagnetic environment,an intelligent anti-jamming decision-making algorithm based on Thompson sampling,Dyna model and expected SARSA learning is proposed.In the expected SARSA learning,Dyna model is applied,and then the convergence speed and steady performance are improved because the reinforcement learning is combined with the model learning.The action selection strategy is further improved by using Thompson sampling algorithm,and Tanh function,which enhances the method's exploration and utilization of the environment.The interference environment corresponding to the time slot is set as the state,and the frequency hopping rate,signal instantaneous bandwidth,frequency sequence and source power are set as actions for constructing state action space,and finally the corresponding frequency hopping system model and reward function are designed.In the complex interference environment where Gaussian white noise,narrowband interference,broadband interference and frequency sweep interference coexist,the simulation results show that this algorithm can balance the both exploration and utilization of the environment and achieves faster convergence speed and stronger anti-interference ability than the compared algorithms.
complex electromagnetic environmentfrequency hopping systemexpect SARSA learningThompson samplingDyna model
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