A Study on Reinforcement Learning for Multi-radar Coexistence Anti-jamming
Aiming at the problem that the multi-radar system cannot work under the frequency sweep interference of the environ-ment,a multi-radar co-existence anti-jamming algorithm based on deep reinforcement learning is studied.In this paper,the envi-ronment is divided into multiple sub-bands,the process of jamming occupying the frequency band is modeled,and the multi-radar system is modeled with Markov model.The double deep q-network(DQN)reinforcement learning algorithm is improved,and com-bined with the gating unit cyclic neural network,so that it can deal with the interference problem that depends on long time series.The deep deterministic strategy reinforcement learning algorithm based on gated recurrent memory is proposed,which improves the network overstaffing and large action set in double DQN reinforcement learning,and adopts the direct output action strategy to ef-fectively reduce the network complexity.The simulation results show that in the case of multiple radar,the algorithm can not only reduce the interference from the outside world,but also reduce the interference between our own radars by avoiding the frequency points with interference.
multi-radar systemdeep reinforcement learninganti-interferenceMarkov modelgated circulation unit
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