Adaptive polynomial least squares slope estimates of noisy data. Issue 7 (3rd October 2021) - Van Hecke, Tanja
Abstract: Noise creates a harmful transformation on the estimation of the derivative of a signal. In this paper, we suggest an easy to implement and adequate approximation method for the derivative function of a signal with noise. It is based on polynomial regression constructed with least squares differences, but with a degree depending on the signal-to-noise ratio. Numerical results suggest similar accuracy compared to established methods as the Savitzsky-Golay smoothing method and the smoothing method based on wavelet transforms.