Denoising method of ESD current waveform based on wavelet and adaptive Kalman filtering

Abstract

In order to reduce the interference noise of ESD current signal, wavelet analysis and adaptive Kalman filter are used in the study of ESD current waveform denoising. The numerical solutions of ESD current of human body-metal model are computed by the Adams prediction-calibration algorithm，and a corresponding noisy ESD current signal model is established. The wavelet denoising method performs denoising performance analysis on this model. Aiming at the measured ESD current waveform, the wavelet threshold denoising method is used to preprocess the ESD current waveforms, and obtain more stable observation data. According to the information of the observed data, the adaptive Kalman filter algorithm proposed by Sage-Husa is used to optimize the data processed the wavelet pretreatment data. The results show that the wavelet analysis and adaptive Kalman filter algorithm can effectively reduce the interference noise of the measured ESD current waveform.

Keywords

• esd
• current waveform
• adams prediction-correction algorithm
• wavelet denoising
• adaptive kalman filter