A Two-Stage Multichannel Chirp Mode Decomposition Method with Energy-Weighted Synchrosqueezing Guidance for Robust Signal Analysis

Purpose: Accurately extracting and decomposing non-stationary, multi-component signals in non-stationary dynamic environments remains a critical challenge. Existing time–frequency analysis (TFA) and signal decomposition techniques often suffer from limitations such as energy diffusion, high sensitivity of initial frequency estimates to noise, and an inability to effectively leverage multi-sensor measurement data fusion. This study aims to address these issues by proposing a Two-Stage Multichannel Chirp Mode Decomposition method with Energy-Weighted Synchrosqueezing Guidance. Methods: The proposed framework adopts a coarse-to-fine strategy comprising two sequential stages. In the first stage, an Energy-Weighted Synchrosqueezing Transform (EST) is introduced. By employing a local energy weighting mechanism to suppress background noise, EST extracts high-confidence initial Instantaneous Frequency (IF) ridges that serve as robust priors. In the second stage, a Unit-Norm Constrained Multichannel Decomposition (UCMD) model is formulated. This model performs deep denoising by exploiting spatial coherence across channels. Guided by the Stage I priors, it achieves accurate joint separation of the signal components. Results: Numerical simulations and experiments confirm the superior robustness of the proposed method in heavy noise. Through practical applications involving structural dynamics and mechanical systems, the framework exhibits exceptional capability in recovering weak, time-varying signal components. Its decomposition accuracy and parameter tracking precision significantly outperform traditional single-channel approaches and standard TFA techniques. Conclusion: The proposed two-stage method successfully overcomes the limitations of existing techniques in high-noise environments, providing a highly robust and generalized data processing algorithm for multichannel dynamic measurement systems operating under complex variable conditions.