Noise Suppression and Ridge Extraction Based on Squeezing Bandwidth for Micro-Doppler Feature Extraction

Passive radar systems, which utilize external illuminators such as broadcast or communication signals, have garnered growing interest due to their cost-effectiveness, low detectability, and anti-jamming capabilities. They typically rely on micro-Doppler (MD) features generated by moving components (e.g., rotors or vibrational elements) for target identification. However, strong noise interference in passive radar environments degrades the quality of MD signals (MDSs). Additionally, the rapid instantaneous frequency (IF) variations exhibited by MDSs result in blurred time-frequency (TF) representations (TFRs), thereby complicating feature extraction. To address these challenges, this paper presents an enhanced TF analysis (TFA) method based on the multisynchrosqueezing transform (MSST). Through the definition of squeezing bandwidth (SB) and the incorporation of constant false alarm rate (CFAR) processing, the proposed method enhances TFR clarity and noise robustness. Building upon the principle of limited IF ridge mutation, we further develop a novel IF ridge extraction scheme employing SB to precisely characterize the IF of MDSs under noisy conditions. Comprehensive simulations and experimental evaluations validate the method’s effectiveness in capturing micro-motion features and processing nonstationary signals.