Real-Time Identification of Periodic Signals using the Recursive Variable Projection Algorithm

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Standard

Real-Time Identification of Periodic Signals using the Recursive Variable Projection Algorithm. / Handler, Johannes; Ninevski, Dimitar; Rollett, Mathias et al.
IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society. 2022. p. 1-6.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Harvard

Handler, J, Ninevski, D, Rollett, M & O'Leary, P 2022, Real-Time Identification of Periodic Signals using the Recursive Variable Projection Algorithm. in IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society. pp. 1-6, 48th Annual Conference of the IEEE Industrial Electronics Society - IECON 2022, Brüssel, Belgium, 17/10/22. https://doi.org/10.1109/IECON49645.2022.9968918

APA

Handler, J., Ninevski, D., Rollett, M., & O'Leary, P. (2022). Real-Time Identification of Periodic Signals using the Recursive Variable Projection Algorithm. In IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society (pp. 1-6) https://doi.org/10.1109/IECON49645.2022.9968918

Vancouver

Handler J, Ninevski D, Rollett M, O'Leary P. Real-Time Identification of Periodic Signals using the Recursive Variable Projection Algorithm. In IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society. 2022. p. 1-6 doi: 10.1109/IECON49645.2022.9968918

Author

Handler, Johannes ; Ninevski, Dimitar ; Rollett, Mathias et al. / Real-Time Identification of Periodic Signals using the Recursive Variable Projection Algorithm. IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society. 2022. pp. 1-6

Bibtex - Download

@inproceedings{198c76f77e7f42ae8fc82c274e55cdf2,
title = "Real-Time Identification of Periodic Signals using the Recursive Variable Projection Algorithm",
abstract = "This paper presents a real-time parameter identification algorithm for periodic signals, based on the recursive variable projection (RVP) algorithm. The recursive implementation enables the tracking of time-varying parameters. The signal model is linear with respect to the amplitude parameters while being nonlinear with respect to the phase and frequency. This feature motivates the use of a variable projection based approach. Its performance is tested using Monte Carlo simulations and the results are compared with those obtained by a multiobjective Gauss-Newton (MGN) algorithm. Furthermore, the RVP algorithm is applied to measurement data acquired by a MEMS accelerometer and it is demonstrated that it can successfully track time-varying linear and nonlinear parameters.",
author = "Johannes Handler and Dimitar Ninevski and Mathias Rollett and Paul O'Leary",
year = "2022",
month = dec,
day = "9",
doi = "10.1109/IECON49645.2022.9968918",
language = "English",
pages = "1--6",
booktitle = "IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society",
note = "48th Annual Conference of the IEEE Industrial Electronics Society - IECON 2022 ; Conference date: 17-10-2022 Through 20-10-2022",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Real-Time Identification of Periodic Signals using the Recursive Variable Projection Algorithm

AU - Handler, Johannes

AU - Ninevski, Dimitar

AU - Rollett, Mathias

AU - O'Leary, Paul

PY - 2022/12/9

Y1 - 2022/12/9

N2 - This paper presents a real-time parameter identification algorithm for periodic signals, based on the recursive variable projection (RVP) algorithm. The recursive implementation enables the tracking of time-varying parameters. The signal model is linear with respect to the amplitude parameters while being nonlinear with respect to the phase and frequency. This feature motivates the use of a variable projection based approach. Its performance is tested using Monte Carlo simulations and the results are compared with those obtained by a multiobjective Gauss-Newton (MGN) algorithm. Furthermore, the RVP algorithm is applied to measurement data acquired by a MEMS accelerometer and it is demonstrated that it can successfully track time-varying linear and nonlinear parameters.

AB - This paper presents a real-time parameter identification algorithm for periodic signals, based on the recursive variable projection (RVP) algorithm. The recursive implementation enables the tracking of time-varying parameters. The signal model is linear with respect to the amplitude parameters while being nonlinear with respect to the phase and frequency. This feature motivates the use of a variable projection based approach. Its performance is tested using Monte Carlo simulations and the results are compared with those obtained by a multiobjective Gauss-Newton (MGN) algorithm. Furthermore, the RVP algorithm is applied to measurement data acquired by a MEMS accelerometer and it is demonstrated that it can successfully track time-varying linear and nonlinear parameters.

U2 - 10.1109/IECON49645.2022.9968918

DO - 10.1109/IECON49645.2022.9968918

M3 - Conference contribution

SP - 1

EP - 6

BT - IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society

T2 - 48th Annual Conference of the IEEE Industrial Electronics Society - IECON 2022

Y2 - 17 October 2022 through 20 October 2022

ER -

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