LCB-CV-UNet: Enhanced Detector for High Dynamic Range Radar Signals
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918218526883840 |
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| author | Wang, Yanbin Chen, Xingyu Wang, Yumiao Wang, Xiang Zang, Chuanfei Cui, Guolong Liu, Jiahuan |
| author_facet | Wang, Yanbin Chen, Xingyu Wang, Yumiao Wang, Xiang Zang, Chuanfei Cui, Guolong Liu, Jiahuan |
| contents | We propose the LCB-CV-UNet to tackle performance degradation caused by High Dynamic Range (HDR) radar signals. Initially, a hardware-efficient, plug-and-play module named Logarithmic Connect Block (LCB) is proposed as a phase coherence preserving solution to address the inherent challenges in handling HDR features. Then, we propose the Dual Hybrid Dataset Construction method to generate a semi-synthetic dataset, approximating typical HDR signal scenarios with adjustable target distributions. Simulation results show about 1% total detection probability improvement with under 0.9% computational complexity added compared with the baseline. Furthermore, it excels 5% over the baseline at the range in 11-13 dB signal-to-noise ratio typical for urban targets. Finally, the real experiment validates the practicality of our model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23454 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LCB-CV-UNet: Enhanced Detector for High Dynamic Range Radar Signals Wang, Yanbin Chen, Xingyu Wang, Yumiao Wang, Xiang Zang, Chuanfei Cui, Guolong Liu, Jiahuan Signal Processing Artificial Intelligence We propose the LCB-CV-UNet to tackle performance degradation caused by High Dynamic Range (HDR) radar signals. Initially, a hardware-efficient, plug-and-play module named Logarithmic Connect Block (LCB) is proposed as a phase coherence preserving solution to address the inherent challenges in handling HDR features. Then, we propose the Dual Hybrid Dataset Construction method to generate a semi-synthetic dataset, approximating typical HDR signal scenarios with adjustable target distributions. Simulation results show about 1% total detection probability improvement with under 0.9% computational complexity added compared with the baseline. Furthermore, it excels 5% over the baseline at the range in 11-13 dB signal-to-noise ratio typical for urban targets. Finally, the real experiment validates the practicality of our model. |
| title | LCB-CV-UNet: Enhanced Detector for High Dynamic Range Radar Signals |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2505.23454 |