NDLI: Kalman Estimation of Single-Tone Parameters and Performance Comparison With MAP Estimator

Content Provider	IEEE Xplore Digital Library
Author	Hua Fu Pooi Yuen Kam
Copyright Year	1991
Abstract	By incorporating a priori knowledge via the a priori probability density function of the frequency and phase, the Kalman estimator for linear, minimum mean-square error estimation of these single-tone parameters in additive, white, Gaussian noise is presented. First, a linear, two-dimensional state-space model for the frequency and phase of a sinusoid is formulated. Then, two Kalman filters are proposed, one based on Tretter's phase noise model [S. A. Tretter, ldquoEstimating the Frequency of a Noisy Sinusoid by Linear Regression,rdquo IEEE Transactions on Information Theory, vol. IT-31, no. 6, pp. 832-835, Nov. 1985], and the other based on our newly proposed model in [H. Fu and P. Y. Kam, ldquoMAP/ML Estimation of the Frequency and Phase of a Single Sinusoid in Noise,rdquo IEEE Transactions on Signal Processing, vol. 55, no. 3, Mar. 2007]. Finally, their mean-square error performances are compared with each other and with that of the maximum a posteriori probability (MAP) estimator, using computer simulations. The results show that the MAP estimator performs best and the Kalman filter based on the improved phase noise model has better performance than that based on Tretter's model, especially at low signal-to-noise ratio.
Sponsorship	IEEE Signal Processing Society
Starting Page	4508
Ending Page	4511
Page Count	4
File Size	441122
File Format	PDF
ISSN	1053587X
Volume Number	56
Issue Number	9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kalman filters Parameter estimation Frequency estimation Phase estimation Phase noise Probability density function Estimation error Additive noise Gaussian noise phase unwrapping Bayesian CramÉr–Rao lower bound (BCRLB) Kalman filter maximum a posteriori (MAP) estimation phase noise model
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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