Improved FMCW Radar Sensing for HAR Systems: Applying Synchroextracting Transform-Enhanced Time–Frequency Representation and Custom Anisotropic CNN

Radar-based human activity recognition (HAR) offers non-contact sensing, environmental robustness, and privacy preservation, but its performance is limited by the quality of nonstationary micro-Doppler signatures in FMCW radar signals. Conventional time-frequency methods, such as STFT and Wigner-Ville distribution, suffer from spectral blurring and cross-term interference, reducing sensing resolution. This letter introduces an improved sensor-oriented time-frequency enhancement framework for FMCW radar HAR using the synchroextracting transform (SET), which selectively extracts energy along instantaneous frequency ridges to suppress some artifacts and enhance micro-Doppler concentration. Applied to clutter-suppressed slow-time signals, SET produces highly concentrated and interpretable time-frequency representations (TFRs), directly improving radar sensing fidelity. The proposed approach is evaluated on the NJUST FMCW radar dataset, where SET-derived TFRs show improved concentration and activity separability over conventional methods. Using a lightweight time-frequency anisotropic radar network (TFARNet), the framework achieves 98.71% accuracy and a 96.14% F1-score with low computational cost (0.119 s per sample), demonstrating that sensor-level time-frequency enhancement leads to measurable gains in FMCW radar HAR-systems for improved sensor based performance.