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

Rik Pintelon (Speaker)

Activity: Talk or presentationTalk or presentation at a conference


Nonparametric frequency response function estimation (FRF) is a first important step towards succesful parametric modelling of the dynamics. In some application 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-nose ratio and the type of excitation on the bias and variance of the FRF estimates
Period9 Jul 201811 Jul 2018
Event title18th IFAC Symposium on System Identification: SYSID 2018
Event typeConference
LocationStockholm, Sweden
Degree of RecognitionInternational