FRF Measurements Subject to Missing Data: Quantification of Noise, Nonlinear Distortion, and Time-Varying Effects

Quantifying the level of nonlinear distortions and time-varying effects in frequency response function measurements is a first step toward the selection of an appropriate parametric model structure. In this paper, we tackle this problem in the presence of missing data, which is an important issue in...

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Bibliographic Details
Published inIEEE transactions on instrumentation and measurement Vol. 68; no. 10; pp. 4175 - 4187
Main Authors Pintelon, Rik, Lataire, John, Vandersteen, Gerd
Format Journal Article
LanguageEnglish
Published New York IEEE 01.10.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Biomedical measurement
Distortion measurement
Estimation
Frequency response
Frequency response function (FRF)
Frequency response functions
Linear systems
Missing data
Nonlinear distortion
nonparametric estimates
Nonparametric statistics
random phase multisine
Remote sensors
time-varying FRF (TV-FRF)
Time-varying systems
Wireless sensor networks
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Summary:Quantifying the level of nonlinear distortions and time-varying effects in frequency response function measurements is a first step toward the selection of an appropriate parametric model structure. In this paper, we tackle this problem in the presence of missing data, which is an important issue in large-scale low-cost wireless sensor networks. The proposed method is based on one experiment with a special class of periodic excitation signals.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2018.2883998