A Low SNR Range Parameter Estimation Method Based on an Improved Parameterized Time-Frequency Analysis Method

In the reconnaissance and jamming of Synthetic Aperture Radar （SAR）， non-cooperative targets are typically the primary subjects of detection. However， due to the difficulty in precisely receiving mainlobe signals during most detection periods， the reception and processing of sidelobe signals become crucial for acquiring target information. When dealing with sidelobe signals， challenges such as prolonged estimation time and low accuracy in parameter estimation arise， primarily because of their extremely low signal-to-noise ratio （SNR） and complex interference conditions. Inaccurate parameter estimation significantly degrades the quality of jamming signal generation， thereby severely compromising the effectiveness of jamming operations. To address these challenges， this study proposes an enhanced parameterized time-frequency analysis method. Specifically targeting the difficulty of range-direction signal parameter estimation under low-SNR conditions， the proposed method introduces a variable window length adjustment to the conventional parameterized time-frequency analysis framework. This improvement enables precise estimation of the range-direction chirp rate， even under severe noise conditions with SNR below -10 dB. The proposed method operates within a parameterized time-frequency analysis framework. After initializing key parameters， it determines an optimal polynomial kernel function and performs high-resolution time-frequency analysis on the target signal. Subsequently， based on the time-frequency distribution， it calculates the concentration metric， extracts ridge lines， and derives the chirp rate estimate. The window length is dynamically adjusted according to the concentration parameter， followed by iterative kernel function refinement to further enhance time-frequency energy concentration. The proposed method was validated through comprehensive simulation experiments involving high-noise environments and signal loss scenarios and comparative analyses with existing techniques. The results demonstrate the method’s feasibility and superior performance under low-SNR sidelobe conditions. Finally， the method’s practical applicability was further confirmed using real-world measured data， proving its capability to accurately estimate target parameters， even in challenging low-SNR observational scenarios.