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Conference Papers Year : 2020

Model-based adaptive filtering of harmonic perturbations applied to high-frequency noninvasive valvometry

Abstract

In this paper, a model-based adaptive filter is used to suppress electrical noise in a high-frequency noninvasive valvometry device, which is part of an autonomous biosensor system using bivalve mollusks valve-activity measurements for ecological monitoring purposes. The proposed model-based adaptive filter uses the dynamic regressor extension and mixing method to allow a decoupled estimation of the parameters. Once the desired regression form of the output model is obtained, a fixed-time estimation approach is used to identify its parameters. By applying these two techniques, a flexible filter structure is obtained with the property of retaining the major relevant components of interest of the original valve-activity signals, even in the case when the unwanted signal frequency components are in the same frequency range as the useful variables.
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Dates and versions

hal-02887927 , version 1 (02-07-2020)

Identifiers

  • HAL Id : hal-02887927 , version 1

Cite

Nelson F Barroso, Rosane Ushirobira, Denis Efimov, Mohamedou Sow, Jean-Charles Massabuau. Model-based adaptive filtering of harmonic perturbations applied to high-frequency noninvasive valvometry. IFAC 2020 - 21st IFAC World Congress, Jul 2020, Berlin, Germany. ⟨hal-02887927⟩
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