Accurate estimation of common sinusoidal parameters in multiple channels

Parameter estimation for exponentially damped complex sinusoids in the presence of white noise using multiple channel measurements is addressed. More precisely, we are interested in the damping factor and frequency parameters which are common among all channels. By exploiting linear prediction and weighted least squares technique, an iterative algorithm is devised to extract the common dynamics of the cisoids. Statistical analysis of the proposed method is studied and confirmed by computer simulations. Moreover, it is shown that the developed estimator attains optimum estimation accuracy and is superior to a conventional subspace-based algorithm when the signal-to-noise ratio is sufficiently high.

[1]  S. Kay Fundamentals of statistical signal processing: estimation theory , 1993 .

[2]  R. O. Schmidt,et al.  Multiple emitter location and signal Parameter estimation , 1986 .

[3]  Sabine Van Huffel,et al.  Common pole estimation in multi-channel exponential data modeling , 2006, Signal Process..

[4]  Guillaume Bouleux,et al.  Common pole estimation with an Orthogonal Vector Method , 2006, 2006 14th European Signal Processing Conference.

[5]  D.G. Dudley,et al.  Dynamic system identification experiment design and data analysis , 1979, Proceedings of the IEEE.

[6]  Steven Kay,et al.  Modern Spectral Estimation: Theory and Application , 1988 .

[7]  Y. Chan,et al.  A parameter estimation approach to estimation of frequencies of sinusoids , 1981 .

[8]  Björn E. Ottersten,et al.  Weighted subspace fitting for general array error models , 1998, IEEE Trans. Signal Process..

[9]  W. M. Carey,et al.  Digital spectral analysis: with applications , 1986 .

[10]  A. Vergult,et al.  Removing Artifacts and Background Activity in Multichannel Electroencephalograms by Enhancing Common Activity , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[11]  Alan J. Laub,et al.  Matrix analysis - for scientists and engineers , 2004 .

[12]  Sudhakar M. Pandit,et al.  Variance of least squares estimators for a damped sinusoidal process , 1994, IEEE Trans. Signal Process..

[13]  Petre Stoica List of references on spectral line analysis , 1993, Signal Process..

[14]  K. C. Ho,et al.  A simple and efficient estimator for hyperbolic location , 1994, IEEE Trans. Signal Process..

[15]  Peter Händel High-order Yule-Walker estimation of the parameters of exponentially damped cisoids in noise , 1993, Signal Process..

[16]  M. Aoki,et al.  On a priori error estimates of some identification methods , 1970 .

[17]  Wim Van Paesschen,et al.  Modeling common dynamics in multichannel signals with applications to artifact and background removal in EEG recordings , 2005, IEEE Transactions on Biomedical Engineering.

[18]  J. Magnus,et al.  Matrix Differential Calculus with Applications in Statistics and Econometrics , 1991 .

[19]  Bart W. Stuck,et al.  A Computer and Communication Network Performance Analysis Primer (Prentice Hall, Englewood Cliffs, NJ, 1985; revised, 1987) , 1987, Int. CMG Conference.

[20]  Petre Stoica,et al.  Spectral Analysis of Signals , 2009 .

[21]  Tapan K. Sarkar,et al.  Matrix pencil method for estimating parameters of exponentially damped/undamped sinusoids in noise , 1990, IEEE Trans. Acoust. Speech Signal Process..

[22]  Yoram Bresler,et al.  Exact maximum likelihood parameter estimation of superimposed exponential signals in noise , 1986, IEEE Trans. Acoust. Speech Signal Process..

[23]  Ramdas Kumaresan,et al.  An algorithm for pole-zero modeling and spectral analysis , 1986, IEEE Trans. Acoust. Speech Signal Process..

[24]  Yngve Selén,et al.  Spectral analysis of multichannel MRS data. , 2005, Journal of magnetic resonance.

[25]  Thomas Kailath,et al.  ESPRIT-A subspace rotation approach to estimation of parameters of cisoids in noise , 1986, IEEE Trans. Acoust. Speech Signal Process..