LCB-CV-UNet: Enhanced Detector for High Dynamic Range Radar Signals
This work addresses detection challenges in radar systems for urban environments, representing an incremental improvement with specific gains.
The paper tackled performance degradation in detecting High Dynamic Range radar signals by proposing LCB-CV-UNet, which improved total detection probability by about 1% with minimal computational overhead and achieved a 5% gain in specific signal-to-noise ratio ranges.
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.