Multi-signal extension of adaptive frequency tracking algorithms

Adaptive tracking of sinusoidal signal components with time-varying amplitudes and frequencies presents a great interest in many engineering applications. In many cases, the frequency components of interest are present in more than one signal. So we propose, in this paper, a simple but efficient approach to extend existing algorithms to track frequency components simultaneously in several signals. Computer simulations and experiments on real signals demonstrate the potential of this approach in terms of estimation variance, convergence speed, and the capability to extract a frequency component common to several signals.

[1]  Ho-En Liao,et al.  Two discrete oscillator based adaptive notch filters (OSC ANFs) for noisy sinusoids , 2005, IEEE Transactions on Signal Processing.

[2]  Samuel D. Stearns,et al.  An adaptive IIR structure for sinusoidal enhancement, frequency estimation, and detection , 1986, IEEE Trans. Acoust. Speech Signal Process..

[3]  Jan Swevers,et al.  Multivariable feedback design to improve tracking performance on tractor vibration test rig , 2007 .

[4]  Frida Sandberg,et al.  Frequency Tracking of Atrial Fibrillation using Hidden Markov Models , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  Marco Luise,et al.  Blind carrier frequency tracking for filterbank multicarrier wireless communications , 2005, IEEE Transactions on Communications.

[6]  Roberto Hornero,et al.  Adaptive modeling and spectral estimation of nonstationary biomedical signals based on Kalman filtering , 2005, IEEE Transactions on Biomedical Engineering.

[7]  Petros Maragos,et al.  Multiband Modulation Energy Tracking for Noisy Speech Detection , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[8]  Michael Ghil,et al.  A mechanistic model of mid-latitude decadal climate variability , 2008 .

[9]  Steven Roman,et al.  Introduction to the Mathematics of Finance , 2004 .

[10]  Kenneth W. Martin,et al.  A second-order hyperstable adaptive filter for frequency estimation , 1993 .

[11]  G. Buzsáki.  Rhythms of the brain , 2006 .

[12]  M. Ferdjallah,et al.  Adaptive digital notch filter design on the unit circle for the removal of powerline noise from biomedical signals , 1994, IEEE Transactions on Biomedical Engineering.

[13]  Hualou Liang,et al.  Empirical mode decomposition of field potentials from macaque V4 in visual spatial attention , 2005, Biological Cybernetics.

[14]  Ramdas Kumaresan,et al.  On decomposing speech into modulated components , 2000, IEEE Trans. Speech Audio Process..

[15]  Gian Domenico Pinna,et al.  Baroreflex Sensitivity: Measurement and Clinical Implications , 2008, Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc.

[16]  Paul Lukowicz,et al.  Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[18]  Jeerasuda Koseeyaporn,et al.  Direct frequency estimation based adaptive algorithm for a second-order adaptive FIR notch filter , 2008, Signal Process..

[19]  U. Zwiener,et al.  Validating phase relations between cardiac and breathing cycles during sleep , 2001, IEEE Engineering in Medicine and Biology Magazine.

[20]  Bengt Carlsson,et al.  A notch filter based on recursive least-squares modelling , 1994, Signal Process..

[21]  Arye Nehorai,et al.  Comparative study of four adaptive frequency trackers , 1997, IEEE Trans. Signal Process..