Direction-of-Arrival Estimation Based on Deep Neural Networks With Robustness to Array Imperfections

By Zhangmeng Liu, Chenwei Zhang, Philip S. Yu. IEEE Transactions on Antennas and Propagation, 2018

482 citations recorded by the source corpus.

Lacking of adaptation to various array imperfections is an open problem for most high-precision direction-of-arrival (DOA) estimation methods. Machine learning-based methods are data-driven, they do not rely on prior assumptions about array geometries, and are expected to adapt better to array imperfections when compared with model-based counterparts. This paper introduces a framework of the deep neural network to address the DOA estimation problem, so as to obtain good adaptation to array imperfections and enhanced generalization to unseen scenarios. The framework consists of a multitask autoencoder and a series of parallel multilayer classifiers. The autoencoder acts like a group of spatial filters, it decomposes the input into multiple components in different spatial subregions. These components thus have more concentrated distributions than the original input, which helps to reduce the burden of generalization for subsequent DOA estimation classifiers. The classifiers follow a one-versus-all classification guideline to determine if there are signal components near preseted directional grids, and the classification results are concatenated to reconstruct a spatial spectrum and estimate signal directions. Simulations are carried out to show that the proposed method performs satisfyingly in both generalization and imperfection adaptation.

Subfields: Civil and Structural Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d52836f43ed15a45d2dd84c0397f61d3b29ce71e

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In Civil and Structural Engineering

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Citations in the corpus

Deep Networks for Direction-of-Arrival Estimation in Low SNR (2020, methodology): “, a multi-layer perceptron (MLP), was employed in [21] with focus on the robustness to array imperfections.”
A Survey of Sound Source Localization with Deep Learning Methods (2021, background): “as [24], [35], [36], [37], [38], also focus on anechoic propagation only, or they do not consider sound sources in audible bandwidth.”