Real-Time Identification of Periodic Signals using the Recursive Variable Projection Algorithm
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IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society. 2022. S. 1-6.
Publikationen: Beitrag in Buch/Bericht/Konferenzband › Beitrag in Konferenzband
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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 -
