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

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

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 inter...

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Bibliographic Details
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
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Institution: Nanyang Technological University
Language: English
Description
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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