A TC‐Based Multicomponent LFM Signal Characterization Method Under Impulsive Noise and Its Application

The burst‐like and high‐amplitude characteristics of impulsive noise, which markedly differ from those of Gaussian noise, render methods based on the Gaussian assumption unable to accurately characterize signals under impulsive noise. Moreover, when dealing with multicomponent signal, existing impulsive noise suppression methods inevitably introduce cross‐term interference. To address these issues, this paper proposes an impulsive noise suppression method based on the torque clustering (TC) algorithm, and thus establishes an accurate representation of multicomponent linear frequency modulation (LFM) signal under impulsive noise. First, the theoretical analysis is conducted to elucidate the inherent limitations of existing noise suppression methods that inevitably cross‐term introduction. A novel impulsive noise suppression technique based on TC is developed, fundamentally eliminating cross‐term interference. Subsequently, two signal representation methods for multicomponent LFM signal under impulsive noise are proposed, namely, TC‐fractional Fourier transform (TC‐FRFT) and TC‐synchrosqueezing transform (TC‐SST). These methods enable accurate characterization of multicomponent LFM signal under impulsive noise, and facilitate precise extraction of signal features. Finally, a mathematical model for parameter estimation of multicomponent LFM signal is established using TC‐FRFT, enabling high‐precision estimation of center frequency and chirp rate in the presence of impulsive noise. Simulation results show that the proposed method can effectively suppress impulsive noise, avoid cross‐term interference in existing methods, and achieve accurate characterization of multicomponent LFM signal under impulsive noise. Furthermore, the proposed TC‐FRFT outperforms existing parameter estimation methods in terms of stability, accuracy, and robustness against noise.