Analysis and Implementation of a Resistance Temperature Estimator Based on Bi-Polynomial Least Squares Method and Discrete Kalman Filter

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This paper presents a bi-polynomial least squares approximation method (LSM) related to the Callendar-Van Dusen (CVD) model to analyse the estimation accuracy. A Kalman filter (KF) is used as a possible alternative for estimations in the presence of noise in the measurements. It is shown that the KF obtained a higher estimation accuracy of the sum of squared residuals (SSR) index with respect to the least squares method (LSM), which minimises the squared estimate of errors (SSE). Thanks to these results, it is possible to choose between LSM or the KF for an adequate fitting using SSE or SSR. A case study is presented, in which this method is shown together with the experimental analysis of the implemented algorithm.

Original languageEnglish
Title of host publication2020 24th International Conference on System Theory, Control and Computing (ICSTCC) : October 8-10, 2020 Sinaia, Romania, Proceedings
EditorsLucian-Florentin Bărbulescu
Number of pages5
Place of PublicationPiscataway
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Publication date08.10.2020
Article number9259767
ISBN (Print)978-1-7281-9810-1
ISBN (Electronic)978-1-7281-9809-5, 978-1-7281-9808-8
Publication statusPublished - 08.10.2020
Event24th International Conference on System Theory, Control and Computing - ICSTCC 2020 - Virtual , Virtual, Sinaia, Romania
Duration: 08.10.202010.10.2020
Conference number: 24

    Research areas

  • Callendar-Van Dusen model, Kalman filter, Least Squares Approximation method, Temperature sensor
  • Engineering