| Summary: | We propose a complex LeDIM-net (C-LeDIM-net) convolutional neural network (CNN) that employs a newly-formulated complex phasor normalization for gridless direction-of-arrival (DOA) estimation. Unlike existing deep learning (DL) approaches, C-LeDIM-net extracts explicit phase information in its intermediate complex-valued feature maps to estimate unknown source DOAs. Given its explicit phase representation, the proposed complex phasor normalization leverages the phase-to-sensor relationship of the feature maps which, as a consequence, improves the robustness of C-LeDIM-net to array imperfections when operating with limited number of snapshots. Simulation results show that the proposed method outperforms the existing methods, including the subspace-based and DL-based methods.
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