LCB-CV-UNet: Enhanced Detector for High Dynamic Range Radar Signals
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 15 references that are in the map. First posted on arXiv 29 May 2025.
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References · 4
- 2021HDR-NeRF: High Dynamic Range Neural Radiance Fieldscited by 114
- 2024FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAscited by 64
- 2018Complex-valued Neural Networks with Non-parametric Activation Functionscited by 19
- 2024Efficient Approaches for GEMM Acceleration on Leading AI-Optimized FPGAscited by 3
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