Robust time-frequency analysis for radar signals using CWD with wavelet denoising and synchrosqueezing

Abstract This study provides a fully automated time-frequency analysis framework for recognizing radar waveforms It aims to overcome the noise sensitivity and the σ trade-off that are typical of the classical Choi–Williams distribution (CWD). Employing wavelet thresholding denoising (db8, global VisuShrink), continuous wavelet decomposition (CWD) with a fixed σ = 1.0, and first-order synchrosqueezing, the proposed methodology yields clear representations that suppress cross-terms, even when faced with significant non-Gaussian interference. Extensive evaluation of fourteen standard radar waveforms, including LFM, NLFM, Frank, Costas, P1–P4, T1–T4, and others, across various noise environments (AWGN, K-distributed colored noise, real X-band sea clutter, and α -stable impulsive noise) shows consistent superiority compared to STFT, WVD, standard CWD, FSST, and SST2. This is demonstrated by a signal-to-noise ratio (SNR) improvement of 7.2–10.2 dB and Rényi entropy values below 4.0, ranging from −15 dB to +15 dB. Synchrosqueezing shows that performance is almost unchanged whether σ is between 0.1 and 10. Using linear SVM classification on HOG features, we achieved an average accuracy of 92.6% (72.8% at −15 dB), which is 3.4 to 8.6 percentage points better than FSST/SST2. The proposed method is designed for immediate use, needing no manual adjustments.