Analysis and Implementation of a Resistance Temperature Estimator Based on Bi-Polynomial Least Squares Method and Discrete Kalman Filter
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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2020 24th International Conference on System Theory, Control and Computing (ICSTCC) : October 8-10, 2020 Sinaia, Romania, Proceedings . ed. / Lucian-Florentin Bărbulescu. Piscataway: IEEE - Institute of Electrical and Electronics Engineers Inc., 2020. p. 614-618 9259767.
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Analysis and Implementation of a Resistance Temperature Estimator Based on Bi-Polynomial Least Squares Method and Discrete Kalman Filter
AU - Schimmack, Manuel
AU - Rehbein, Jan Philip
AU - Mercorelli, Paolo
N1 - Conference code: 24
PY - 2020/10/8
Y1 - 2020/10/8
N2 - 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.
AB - 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.
KW - Callendar-Van Dusen model
KW - Kalman filter
KW - Least Squares Approximation method
KW - Temperature sensor
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=85097973712&partnerID=8YFLogxK
U2 - 10.1109/ICSTCC50638.2020.9259767
DO - 10.1109/ICSTCC50638.2020.9259767
M3 - Article in conference proceedings
AN - SCOPUS:85097973712
SN - 978-1-7281-9810-1
SP - 614
EP - 618
BT - 2020 24th International Conference on System Theory, Control and Computing (ICSTCC)
A2 - Bărbulescu, Lucian-Florentin
PB - IEEE - Institute of Electrical and Electronics Engineers Inc.
CY - Piscataway
T2 - 24th International Conference on System Theory, Control and Computing - ICSTCC 2020
Y2 - 8 October 2020 through 10 October 2020
ER -