Antenna Failure Resilience: Deep Learning-Enabled Robust DOA Estimation with Single Snapshot Sparse Arrays
arxiv(2024)
摘要
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 real-world dataset employed in this study are available at
https://github.com/ruxinzh/Deep_RSA_DOA.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要