Frequency Response Function Identification from Incomplete Data: A Wavelet-based Approach

Nic Dirkx, Koen Tiels, Tom Oomen

Research output: Contribution to journalConference articleScientificpeer-review

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Frequency Response Function (FRF) identification plays a crucial role in the design, the control, and the analysis of complex dynamical systems, including thermal and motion systems. Especially for applications that require long measurements, missing data samples, e.g., due to interruptions in the data transmission or sensor failure, often occur. The aim of this paper is to accurately identify nonparametric FRF models of periodically excited systems from noisy output measurements with missing samples. The presented method employs a wavelet-based transformation to address the identification problem in the time-frequency plane. A simulation example confirms that the developed techniques produce accurate estimates, even when many samples are missing.

Original languageEnglish
Pages (from-to)439-444
Issue number37
Publication statusPublished - 2022
Event2nd Modeling, Estimation and Control Conference, MECC 2022 - Jersey City, United States
Duration: 2 Oct 20225 Oct 2022


  • Frequency domain identification
  • linear systems
  • missing data
  • non-parametric methods
  • transient estimation


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