Tracking characteristics of the constrained IIR adaptive notch filter

In this paper, we study the tracking behavior of a constrained IIR notch filter whose coefficients are estimated using a constant step size Gauss-Newton algorithm. Using weak convergence theory and the concept of prescaling, it is shown that the mean behavior of the adaptive filter coefficients can be studied by using an ordinary differential equation (ODE). Approximate and simple closed form results are derived for the tracking behavior of a second order notch filter. Computer simulations are presented to substantiate the analysis.

[1]  Harold J. Kushner,et al.  Weak Convergence and Properties of Adaptive Asymptotic F ilters w ith 177 Constant Gains , 1998 .

[2]  Steven Kay,et al.  The effects of noise on the autoregressive spectral estimator , 1979 .

[3]  H. Kushner,et al.  Asymptotic Properties of Stochastic Approximations with Constant Coefficients. , 1981 .

[4]  Bhaskar D. Rao,et al.  Tracking analysis of an ARMA parameter estimation algorithm using weak convergence theory , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[5]  Albert Benveniste Design of monostep and multistep adaptive algorithms for the tracking of time varying systems , 1984, The 23rd IEEE Conference on Decision and Control.

[6]  Thomas Kailath,et al.  Optimized lattice-form adaptive line enhancer for a sinusoidal signal in broad-band noise , 1981 .

[7]  Tung Ng,et al.  Some aspects of an adaptive digital notch filter with constrained poles and zeros , 1987, IEEE Trans. Acoust. Speech Signal Process..

[8]  A. Benveniste,et al.  A measure of the tracking capability of recursive stochastic algorithms with constant gains , 1982 .

[9]  B. Widrow,et al.  Stationary and nonstationary learning characteristics of the LMS adaptive filter , 1976, Proceedings of the IEEE.

[10]  B. Widrow,et al.  Adaptive noise cancelling: Principles and applications , 1975 .

[11]  J. Zeidler,et al.  Second-order output statistics of the adaptive line enhancer , 1979 .

[12]  J. Treichler,et al.  Transient and convergent behavior of the adaptive line enhancer , 1979 .

[13]  B. Friedlander A recursive maximum likelihood algorithm for ARMA line enhancement , 1982 .

[14]  Arye Nehorai A minimal parameter adaptive notch filter with constrained poles and zeros , 1985, IEEE Trans. Acoust. Speech Signal Process..

[15]  Lennart Ljung,et al.  A theoretical analysis of recursive identification methods , 1978, Autom..

[16]  D. Farden Tracking properties of adaptive signal processing algorithms , 1981 .

[17]  J. Glover Adaptive noise canceling applied to sinusoidal interferences , 1977 .

[18]  R.V. Raja Kumar,et al.  A gradient algorithm for center-frequency adaptive recursive bandpass filters , 1985, Proceedings of the IEEE.

[19]  D. M. Chabries,et al.  Adaptive enhancement of multiple sinusoids in uncorrelated noise , 1978 .

[20]  S. Kung,et al.  Adaptive notch filtering for the retrieval of sinusoids in noise , 1984 .

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

[22]  N. Bershad,et al.  Tracking characteristics of the LMS adaptive line enhancer-response to a linear chirp signal in noise , 1980 .

[23]  Neil J. Bershad,et al.  ALE behavior for two sinusoidal signal models , 1985, IEEE Trans. Acoust. Speech Signal Process..

[24]  L. Griffiths Rapid measurement of digital instantaneous frequency , 1975 .

[25]  Julius O. Smith,et al.  Analysis and performance evaluation of an adaptive notch filter , 1983, The 22nd IEEE Conference on Decision and Control.

[26]  Eweda Eweda,et al.  Tracking error bounds of adaptive nonstationary filtering , 1985, Autom..