Direction of Arrival Estimation Algorithm for Underwater Distributed Sources Based on Deep Neural Network

In view of the limitation of traditional subspace-based direction of arrival(DOA) estimation algorithms that rely on prior coherence information for localizing distributed sources with varying coherences, a DOA estimation algorithm for underwater distributed sources based on deep neural network(DNN) was proposed in this paper. By leveraging the separability of temporal and angular coherence components in the partially coherent distributed source signal model and simplifying the model by segmented mean normalization, a DNN model was constructed and trained with multiple samples of different coherence coefficients, thus achieving robust DOA estimation for distributed sources with different coherence levels. Simulation results indicate that the proposed method can effectively estimate the distributed source parameters with different coherent coefficients without relying on coherence prior knowledge. The proposed method is compared with four traditional subspace-based methods and one deep convolutional neural network algorithm, and the results show that the root mean square error of the proposed method under the coherently distributed source case is 0.42° lower than that of other methods under different signal-to-noise ratios(SNRs) and snapshots; under the incoherently distributed source case, the RMSE of the proposed method is 0.04° lower than that of other methods with SNR greater than 0 dB and snapshots greater than 600. In the range of full coherence coefficients, the proposed methods all show better estimation performance, which proves their applicability in complex underwater environments.