Antenna Failure Resilience: Deep Learning-Enabled Robust DOA Estimation with Single Snapshot Sparse Arrays

Recent advancements in Deep Learning (DL) for Direction of Arrival (DOA) estimation have highlighted its superiority over traditional methods, offering faster inference, enhanced super-resolution, and robust performance in low Signal-to-Noise Ratio (SNR) environments. Despite these advancements, exi...

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Bibliographic Details
Published in:Conference record - Asilomar Conference on Signals, Systems, & Computers pp. 84 - 89
Main Authors: Zheng, Ruxin, Sun, Shunqiao, Liu, Hongshan, Chen, Honglei, Soltanalian, Mojtaba, Li, Jian
Format: Conference Proceeding
Language:English
Published: IEEE 27.10.2024
Subjects:
Antenna arrays
antenna failure
Automotive radar
Deep learning
Direction-of-arrival estimation
DOA estimation
Estimation
Radar
Radar antennas
Signal resolution
Signal to noise ratio
single snapshot
sparse arrays
Superresolution
Vehicle dynamics
ISSN:2576-2303
Online Access:Get full text
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Summary:Recent advancements in Deep Learning (DL) for Direction of Arrival (DOA) estimation have highlighted its superiority over traditional methods, offering faster inference, enhanced super-resolution, and robust performance in low Signal-to-Noise Ratio (SNR) environments. Despite these advancements, existing research predominantly focuses on multi-snapshot scenarios, a limitation in the context of automotive radar systems which demand high angular resolution and often rely on limited snapshots, sometimes as scarce as a single snapshot. Furthermore, the increasing interest in sparse arrays for automotive radar, owing to their cost-effectiveness and reduced antenna element coupling, presents additional challenges including susceptibility to random sensor failures. This paper introduces a pioneering DL framework featuring a sparse signal augmentation layer, meticulously crafted to bolster single snapshot DOA estimation across diverse sparse array setups and amidst antenna failures. To our best knowledge, this is the first work to tackle this issue. Our approach improves the adaptability of deep learning techniques to overcome the unique difficulties posed by sparse arrays with single snapshot. We conduct thorough evaluations of our network's performance using simulated and real-world data, showcasing the efficacy and real-world viability of our proposed solution. The code and the real-world dataset are available at https://github.com/ruxinzh/Deep_RSA_DOA.
ISSN:2576-2303
DOI:10.1109/IEEECONF60004.2024.10943029