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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| Published in | IEEE transactions on instrumentation and measurement Vol. 68; no. 10; pp. 4175 - 4187 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.10.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0018-9456 1557-9662 |
| DOI | 10.1109/TIM.2018.2883998 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Pintelon, Rik Vandersteen, Gerd Lataire, John |
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| SubjectTerms | 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 |
| Title | FRF Measurements Subject to Missing Data: Quantification of Noise, Nonlinear Distortion, and Time-Varying Effects |
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