Parameter Identification of Coulomb Oscillator from Noisy Sensor Data

Guddu Kumar, Vikash Kumar Mishra, R. Swaminathan, Abhinoy Kumar Singh

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Coulomb oscillator is used for analyzing several real-life systems, which demand a precise modeling of the oscillation. The modeling is based on the stochastic estimation of unknown parameters of the model representing the oscillation. This paper introduces a Bayesian approach for the estimation of unknown parameters from sensor-generated noisy data. Among several Bayesian approaches, Gaussian filtering approach is most popular. A major challenge that appeared with the Gaussian filtering is intractable integral, which is approximated numerically. Several Gaussian filters have been reported by using different numerical approximation methods. This paper implements a popular Gaussian filter, named as cubature Kalman filter (CKF), for the estimation of unknown parameters. The CKF uses a third-degree spherical radial rule for the numerical approximation of the intractable integrals. Simulation results conclude a high accuracy of the CKF-based estimate of unknown parameters.

Original languageEnglish
Title of host publicationSmart Innovation, Systems and Technologies
PublisherSpringer Science and Business Media Deutschland GmbH
Pages327-338
Number of pages12
DOIs
StatePublished - 1 Jan 2022
Externally publishedYes

Publication series

NameSmart Innovation, Systems and Technologies
Volume229
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Keywords

  • Bayesian filtering
  • Coulomb oscillator
  • Cubature Kalman filter
  • Nonlinear filtering
  • Parameter estimation

ASJC Scopus subject areas

  • Decision Sciences (all)
  • Computer Science (all)

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