Deep Residual Learning Based Localization of Near-Field Sources in Unknown Spatially Colored Noise Fields

In this paper, we explore the problem of near-field source localization in an unknown spatially colored noise environment using an end-to-end neural network which is based on deep residual learning. Specifically, the proposed approach uses the multi-dimensional information of the array covariance as input, and finally directly outputs the location information of the near-field sources through the regression structure. The architecture of deep neural network is well designed taking into account the trade-off between the expression ability and computational complexity. In addition, benefiting from the method of generating training data that combines the degree of separation to traverse the spatial location, the proposed approach has a robust performance for different location parameter separation. The simulation results demonstrate that the proposed approach outperforms the existing model-driven methods under various conditions, especially for the adverse scenes with low SNRs, small number of snapshots, or correlated sources.