In this paper, a recursive Gauss-Newton (RGN) algorithm is first developed for adaptive tracking of the amplitude, frequency and phase of a real sinusoid signal in additive white noise. The derived algorithm is then simplified for computational complexity reduction as well as improved with the use of multiple forgetting factor (MFF) technique to provide a flexible way of keeping track of the parameters with different rates. The effectiveness of the simplified MFF-RGN scheme in sinusoidal parameter tracking is demonstrated via computer simulations.
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Md. Tawfiq AMIN, Kenneth Wing-Kin LUI, Hing-Cheung SO, "Fast Tracking of a Real Sinusoid with Multiple Forgetting Factors" in IEICE TRANSACTIONS on Fundamentals,
vol. E91-A, no. 11, pp. 3374-3379, November 2008, doi: 10.1093/ietfec/e91-a.11.3374.
Abstract: In this paper, a recursive Gauss-Newton (RGN) algorithm is first developed for adaptive tracking of the amplitude, frequency and phase of a real sinusoid signal in additive white noise. The derived algorithm is then simplified for computational complexity reduction as well as improved with the use of multiple forgetting factor (MFF) technique to provide a flexible way of keeping track of the parameters with different rates. The effectiveness of the simplified MFF-RGN scheme in sinusoidal parameter tracking is demonstrated via computer simulations.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e91-a.11.3374/_p
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@ARTICLE{e91-a_11_3374,
author={Md. Tawfiq AMIN, Kenneth Wing-Kin LUI, Hing-Cheung SO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Fast Tracking of a Real Sinusoid with Multiple Forgetting Factors},
year={2008},
volume={E91-A},
number={11},
pages={3374-3379},
abstract={In this paper, a recursive Gauss-Newton (RGN) algorithm is first developed for adaptive tracking of the amplitude, frequency and phase of a real sinusoid signal in additive white noise. The derived algorithm is then simplified for computational complexity reduction as well as improved with the use of multiple forgetting factor (MFF) technique to provide a flexible way of keeping track of the parameters with different rates. The effectiveness of the simplified MFF-RGN scheme in sinusoidal parameter tracking is demonstrated via computer simulations.},
keywords={},
doi={10.1093/ietfec/e91-a.11.3374},
ISSN={1745-1337},
month={November},}
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TY - JOUR
TI - Fast Tracking of a Real Sinusoid with Multiple Forgetting Factors
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 3374
EP - 3379
AU - Md. Tawfiq AMIN
AU - Kenneth Wing-Kin LUI
AU - Hing-Cheung SO
PY - 2008
DO - 10.1093/ietfec/e91-a.11.3374
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E91-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2008
AB - In this paper, a recursive Gauss-Newton (RGN) algorithm is first developed for adaptive tracking of the amplitude, frequency and phase of a real sinusoid signal in additive white noise. The derived algorithm is then simplified for computational complexity reduction as well as improved with the use of multiple forgetting factor (MFF) technique to provide a flexible way of keeping track of the parameters with different rates. The effectiveness of the simplified MFF-RGN scheme in sinusoidal parameter tracking is demonstrated via computer simulations.
ER -