A vacuum ultraviolet signal denoising method based on GCV-GWO combined wavelet threshold

In space, the detection of vacuum ultraviolet (VUV) signals occurs in a complex space environment with multi-physical field coupling, resulting in significant noise contamination. To address the denoising requirements in such scenarios, this study puts forward a modified wavelet threshold denoising algorithm. The noisy signal is first subjected to decomposition, and the GCV (Generalized Cross-Validation) function serves to select the optimal wavelet basis function as well as the decomposition level. The GWO (Grey Wolf Optimizer) is then integrated to find the optimal threshold for each layer of wavelet decomposition. For the purpose of verifying the effectiveness of the proposed method, simulation analyses were conducted using both simulated signals and solar EUV spectral irradiance data released by the NCEI. The outcomes of the experiment reveal that when contrasted with similar denoising approaches, the proposed method can better remove noise from signals while preserving useful components in the signals.