An algebraic approach to on-line signal denoising and derivatives estimation

Kasać, Josip and Majetić, Dubravko and Brezak, Danko (2018) An algebraic approach to on-line signal denoising and derivatives estimation. = An algebraic approach to on-line signal denoising and derivatives estimation. Journal of the Franklin Institute, 355 (15). pp. 7799-7825. ISSN 0016-0032. Vrsta rada: ["eprint_fieldopt_article_type_article" not defined]. Kvartili JCR: Q1 (2017). Točan broj autora: 3.

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In this paper, a new algebraic approach to the on-line signal derivatives estimation is proposed. The proposed approach is based on the conversion of the truncated Taylor series expansion to the set of linearly independent equations regarding the signal derivatives. The nonhomogeneous parts of the obtained set of equations are convolution integrals, which can be transformed to the stable linear state-space filter realization. The proposed algebraic estimator provides stable convergence without the need for periodic re-initialization, as in the case of the conventional algebraic estimators. In contrast to the Taylor series-based tracking differentiators, the proposed estimator also provides an estimation of the arbitrary number of the higher-order signal derivatives. In addition, the tuning of the estimator parameters does not depends on the filter dimension. The efficiency of the proposed estimator is illustrated by the simulation examples and experimental results related to the monitoring of the surgical drilling process.

Item Type: Article (["eprint_fieldopt_article_type_article" not defined])
Keywords (Croatian): algebraic estimation; differentiators; signal denoising; linear systems; continuous-time filters
Subjects: TECHNICAL SCIENCE > Mechanical Engineering
Divisions: 900 Department of Robotics and Production System Automation > 910 Chair of Engineering Automation
Indexed in Web of Science: Yes
Indexed in Current Contents: Yes
Citations JCR: 0 (05.11.2018.)
Quartiles: Q1 (2017)
Date Deposited: 19 Sep 2018 10:46
Last Modified: 14 Dec 2018 13:21

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