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 compu-tational 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.