Adaptive Range–Doppler Representation Enhancement with Lightweight Dual-View Detection for FMCW Radar Under Simulated Compound Interference

Compound noise, clutter, and multipath reduce target–background separability in frequency-modulated continuous-wave (FMCW) radar Range–Doppler (RD) maps. Rawonly processing can leave interference-obscured weak targets insufficiently salient, whereas replacing Raw with an enhanced representation or directly mixing the two views at the input stage may attenuate useful measurement structure and introduce enhancement artifacts prematurely. To address this bottleneck, an Enhanced saliency view aligned with the original physical RD coordinates is first constructed using local median background estimation, median absolute deviation (MAD) normalization, and adaptive zero-Doppler weighting. Raw is then retained as the primary observation and encoded in parallel with Enhanced by two independent shallow branches, avoiding premature information mixing. Finally, their stride-four features are concatenated and projected through a 1 × 1 convolution to realize lightweight late fusion of the original RD structure and local target saliency. Three independently generated simulation datasets are used: Dataset A for model development, and Datasets B and C for repeated evaluation with five training seeds per dataset. Compared with the Raw baseline, Raw–Enhanced increases the mean F1 score from 0.8347 to 0.8470, corresponding to a relative improvement of 1.47%, and increases recall from 0.7470 to 0.7662 while maintaining essentially unchanged precision. The conditional range and velocity RMSEs over successfully matched detections decrease by 11.71% and 13.05%, respectively. With 10,793 trainable parameters and 27.33 MFLOPs, the proposed detector is suitable for lightweight single-frame 77 GHz one-transmitter/one-receiver FMCW moving-target range–velocity detection in complex noise, clutter, and multipath backgrounds.