TY - JOUR
T1 - Analysis and implementation of the adaptive notch filter for frequency estimation
AU - Kung, Sun Yuan
AU - Rao, D. V.Bhaskar
N1 - Funding Information:
applications to a special class of signals. This paper focusses on a particular and yet a very important class of spectrum analysis problems: the :cThis research has been supported in part by the Office of Naval Research under Contract No. N0014.- 81—K—0191; ONR Contract No. N0014—80—C—0457; and by Army Research Office under Grant No. DAA(2 29—79—C— 0054.
Publisher Copyright:
© 1982 IEEE.
PY - 1982
Y1 - 1982
N2 - This paper enhances some theoretical and implementation aspects of a constrained autoregressive moving average model, the notch filter model developed in [1] for the estimation of sinusoidal signals in additive, uncorrelated noise, colored or white. This model is shown to approximate the actual signal plus noise model. In addition, the parameter estimates obtained by minimization of the output power of the notch filter approximate the maximum likelihood estimate of the model parameters. The relationship of the notch filtering approach to the existing autoregressive and Pisarenke methods is established. Next, a scheme to combine fast convergence and unbiased estimation is suggested. Lastly, certain implementation aspects of the filter are considered and the method is shown to be amenable to parallel processing.
AB - This paper enhances some theoretical and implementation aspects of a constrained autoregressive moving average model, the notch filter model developed in [1] for the estimation of sinusoidal signals in additive, uncorrelated noise, colored or white. This model is shown to approximate the actual signal plus noise model. In addition, the parameter estimates obtained by minimization of the output power of the notch filter approximate the maximum likelihood estimate of the model parameters. The relationship of the notch filtering approach to the existing autoregressive and Pisarenke methods is established. Next, a scheme to combine fast convergence and unbiased estimation is suggested. Lastly, certain implementation aspects of the filter are considered and the method is shown to be amenable to parallel processing.
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U2 - 10.1109/ICASSP.1982.1171784
DO - 10.1109/ICASSP.1982.1171784
M3 - Conference article
AN - SCOPUS:17444376922
SN - 0736-7791
VL - 1982-May
SP - 663
EP - 666
JO - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
JF - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
M1 - 1171784
T2 - 1982 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1982
Y2 - 3 May 1982 through 5 May 1982
ER -