Gridless DOA estimation using complex-valued convolutional neural network with phasor normalization

• Main Authors: Tan, Zhi-Wei, Liu, Yuan, Khong, Andy Wai Hoong, Nguyen, Anh H. T.
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Electrical and electronic engineering; Array Imperfections; Complex Neural Network
• Online Access: https://hdl.handle.net/10356/171213
• Institution: Nanyang Technological University

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.