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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| Vydáno v: | Conference record - Asilomar Conference on Signals, Systems, & Computers s. 84 - 89 |
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IEEE
27.10.2024
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| ISSN: | 2576-2303 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Zheng, Ruxin Liu, Hongshan Sun, Shunqiao Soltanalian, Mojtaba Chen, Honglei Li, Jian |
| Author_xml | – sequence: 1 givenname: Ruxin surname: Zheng fullname: Zheng, Ruxin organization: University of Illinois Chicago,Department of Electrical and Computer Engineering,Tuscaloosa,AL,35487 – sequence: 2 givenname: Shunqiao surname: Sun fullname: Sun, Shunqiao organization: University of Illinois Chicago,Department of Electrical and Computer Engineering,Tuscaloosa,AL,35487 – sequence: 3 givenname: Hongshan surname: Liu fullname: Liu, Hongshan organization: University of Illinois Chicago,Department of Electrical and Computer Engineering,Tuscaloosa,AL,35487 – sequence: 4 givenname: Honglei surname: Chen fullname: Chen, Honglei organization: Mathworks,Natick,MA,01760 – sequence: 5 givenname: Mojtaba surname: Soltanalian fullname: Soltanalian, Mojtaba organization: University of Illinois Chicago,Department of Electrical and Computer Engineering,Chicago,IL,60607 – sequence: 6 givenname: Jian surname: Li fullname: Li, Jian organization: University of Florida,Department of Electrical and Computer Engineering,Gainesville,FL,32611 |
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| Snippet | Recent advancements in Deep Learning (DL) for Direction of Arrival (DOA) estimation have highlighted its superiority over traditional methods, offering faster... |
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| SubjectTerms | 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 |
| Title | Antenna Failure Resilience: Deep Learning-Enabled Robust DOA Estimation with Single Snapshot Sparse Arrays |
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