Imperial College London
Portrait Picture

ProfessorThomasParisini

Faculty of Engineering, Department of Electrical and Electronic Engineering

Chair in Industrial Control, Head of Group for CAP
 
 
 
//

Contact

 

+44 (0)20 7594 6240t.parisini Website

 
 
//

Location

 

1114Electrical EngineeringSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Chen:2015:10.1002/acs.2576,
author = {Chen, B and Pin, G and Ng, WM and Hui, SYR and Parisini, T},
doi = {10.1002/acs.2576},
journal = {International Journal of Adaptive Control and Signal Processing},
pages = {1591--1608},
title = {A parallel prefiltering approach for the identification of a biased sinusoidal signal: theory and experiments},
url = {http://dx.doi.org/10.1002/acs.2576},
volume = {29},
year = {2015}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The problem of estimating the amplitude, frequency, and phase of an unknown sinusoidal signal from a noisy-biased measurement is addressed in this paper by a family of parallel prefiltering schemes. The proposed methodology consists in using a pair of linear filters of specified order to generate a suitable number of auxiliary signals that are used to estimate—in an adaptive way—the frequency, the amplitude, and the phase of the sinusoid. Increasing the order of the prefilters improves the noise immunity of the estimator, at the cost of an increase of the computational complexity. Among the whole family of estimators realizable by varying the order of the filters, the simple parallel prefilters of orders 2 + 2 and 3 + 3 are discussed in detail, being the most attractive from the implementability point of view. The behavior of the two algorithms with respect to bounded external disturbances is characterized by input-to-state stability arguments. Finally, the effectiveness of the proposed technique is shown both by comparative numerical simulations and by a real experiment addressing the estimation of the frequency of the electrical mains from a noisy voltage measurement.
AU  - Chen,B
AU  - Pin,G
AU  - Ng,WM
AU  - Hui,SYR
AU  - Parisini,T
DO  - 10.1002/acs.2576
EP  - 1608
PY  - 2015///
SN  - 1099-1115
SP  - 1591
TI  - A parallel prefiltering approach for the identification of a biased sinusoidal signal: theory and experiments
T2  - International Journal of Adaptive Control and Signal Processing
UR  - http://dx.doi.org/10.1002/acs.2576
UR  - http://hdl.handle.net/10044/1/31300
VL  - 29
ER  -
Download