Publications
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Smoothing the LPM Estimate of the Frequency Response Function Via an Impulse Response Truncation Technique Vrije Universiteit Brussel
A statistical impulse response truncation technique is applied to the local polynomial method (LPM)-estimate of the frequency response function (FRF), resulting in an improved, smooth FRF. Formulated as a nonparametric linear-least-squares-estimate, the LPM is first applied to estimate the FRF from a full data record of a single-input-single-output system, systematically expressed in an output-error framework. The smooth characteristics of both ...
Nonparametric Data-Driven Modeling of Linear Systems ESTIMATING THE FREQUENCY RESPONSE AND IMPULSE RESPONSE FUNCTION KU Leuven Vrije Universiteit Brussel
Optimal Settings for Measuring Frequency Response Functions with Weighted Overlapped Segment Averaging Vrije Universiteit Brussel
This paper investigates the measurement errors involved in estimating frequency response functions (FRFs) - and related quantities such as the coherence function - from weighted overlapped segment averaging, a technique that has become a standard in modern data analyzers due to its computational advantages. A particular attention is paid to leakage errors, for which this technique has frequently been criticized. Our main result is that a ...
Impact of the Missing Data Pattern, the Oversampling, the Noise Level and the Excitation on Nonparametric Frequency Response Function Estimates Vrije Universiteit Brussel KU Leuven
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 ...
Frequency Response Matrix Estimation From Partially Missing Data—for Periodic Inputs Vrije Universiteit Brussel KU Leuven
Multivariate nonparametric frequency response estimation is important in engineering. It allows one to get a quick insight into the dynamics of the system from input-output measurements. Sometimes, measurements can be missing due to faulty sensors or communication links. In this paper, we develop a method that estimates the frequency response matrix together with the missing samples from partially known data. In addition, the method can estimate ...
Frequency response function estimation in the presence of missing output data Vrije Universiteit Brussel KU Leuven
Frequency response function (FRF) estimation is part of nonparametric system identification in the frequency domain. The FRF measurements give a quick but deep insight into the dynamics of complex systems. Data samples can get lost in some applications due to sensor failure and/or data transmission errors. We want to overcome this problem without having to repeat the measurement and/or the experiment because this can be either impossible or too ...
Detecting a time-varying behavior in frequency response function measurements Vrije Universiteit Brussel
This paper provides data-driven tools to detect and quantify approximately the influence of the time variation of a system under test in classical frequency response function (FRF) measurements. To achieve this, the best linear time-invariant approximation of a linear time-varying system is defined and is estimated using existing FRF estimators. An analysis of the residuals of the latter estimation reveals the frequency band in which the ...
Frequency Response Matrix Estimation From Missing Input–Output Data Vrije Universiteit Brussel KU Leuven
Frequency response matrix (FRM) estimation is an important preprocessing step in system identification. This nonparametric step can give a quick insight into the behavior of a dynamic system without making too many assumptions. Sensor failures or faulty communication links make the measurement data go missing. In this paper, a nonparametric method is developed that identifies a multiple-input-multiple-output system from data with missing samples ...