Impact of the Missing Data Pattern, the Oversampling, the Noise Level, and the Excitation on Nonparametric Frequency Response Function Estimates

© 2018 Nonparametric frequency response function estimation (FRF) is a first important step towards successful parametric modelling of the dynamics. In some applications such as, for example, low-cost wireless sensor networks, sensors are subject to failure (clipping, outliers) and the transmission errors of the wireless communication can be as high as 30%. Hence, nonparametric estimation of the FRF in the presence of missing data is an important issue. In this paper we study the impact of the missing data pattern, the missing data fraction, the oversampling (w.r.t. the bandwidth of the system), the signal-to-noise ratio and the type of excitation on the bias and variance of the FRF estimates.

Publication year:2018