Small Sample Coherent DOA Estimation Method Based on S2S Neural Network Meta Reinforcement Learning

Aiming at the existing Direction of Arrival (DOA) methods based on neural network, a large number of samples are required to achieve signal-scene adaptation and accurate angle estimation. In the coherent signal environment, the problems of a larger amount of training sample data are required. In this paper, the DOA of coherent signal is converted into the DOA parameter estimation of the angle interval of incident signal. The accurate estimation of coherent DOA under the condition of small samples based on meta-reinforcement learning (MRL) is realized. The meta-reinforcement learning method in this paper models the process of angle interval estimation of coherent signals as a Markov decision process. In the inner loop layer, the sequence to sequence (S2S) neural network is used to express the angular interval feature sequence of the incident signal DOA. The strategy learning of the existence of angle interval under small samples is realized through making full use of the context relevance of spatial spectral sequence through S2S neural network. Thus, according to the optimal strategy, the output sequence is sequentially determined to give the angle interval of the incident signal. Finally, DOA is obtained through one-dimensional spectral peak search according to the angle interval obtained. The experiment shows that the meta-reinforcement learning algorithm based on S2S neural network can quickly converge to the optimal state by only updating the gradient of S2S neural network parameters with a small sample set when a new signal environment appears.