Adaptive Vibrarthographic Signal Denoising via Ant Colony Optimization Using Dynamic Denoising Filter Parameters

This study proposes an ant colony optimization (ACO) denoising method with dynamic filter parameters. The proposed method is developed based on ensemble empirical mode decomposition (EEMD), and aims to improve the quality of vibrarthographic (VAG) signals. It mixes the original VAG signals with different white noise amplitudes, and adopts a hybrid technology that combines EEMD with a Savitzky-Golay (SG) filter containing the dynamic parameters optimized by ACO. The results show that the proposed method provides a higher peak signal-to-noise ratio (PSNR) and a smaller root-mean-square difference than the regular methods. The SNR improvement for the VAG signals of normal knees can reach 13 dB while maintaining the original signal structure, and the SNR improvement for the VAG signals of abnormal knees can reach 20 dB. The method proposed in this study can improve the quality of nonstationary VAG signals.

SNR improvement based on piecewise linear interpolation

Interpolation improves the resolution of the curve. Based on the stationary characteristics of the signal and the non-stationary characteristics of the noise, the theoretical proof indicates that the piecewise linear interpolation can improve the signal-to-noise ratio, which is further confirmed by simulation results.