Direction finding for coherent sources with deep hybrid neural networks

In this paper, an intelligent direction finding (DF) architecture based on hybrid neural network is proposed. Firstly, the array outputs of DF system are multiplied by the corresponding ideal array manifold and a high-dimensional preprocessed feature vector is derived. In the next, the preprocessed feature is straightly fed into the auto-encoder. The auto-encoder outputs are then used as the input of the cascaded deep residual neural network in order to extract spatial spectral of sources. Finally, the direction-of-arrival (DOA) results are derived according to the locations of the normalised spectral peaks. There are two advantages with our proposed network architecture. One is the DF estimation accuracy is independent on the array aperture size when the number of elements is fixed. And the other is that the proposed network structure and processing procedures can be directly generalised into unknown scenarios but having higher angle resolution capacity at the same time. Finally, extensive numerical experiments illustrate the correctness and potential advantages of the proposed deep hybrid neural network.