NDLI: Recursive Bayes risk parameter estimation from the cyclic autocorrelation matrix

Content Provider	IEEE Xplore Digital Library
Author	Riba-Sagarra, J. Vazquez-Grau, G.
Copyright Year	1994
Description	Author affiliation: Dept. of Signal Theor. & Commun., Polytechnic Univ. of Catalonia, Barcelona, Spain (Riba-Sagarra, J.; Vazquez-Grau, G.)
Abstract	We present a new method for recursively estimating the frequency and timing parameters of a second order cyclostationary signal with known cyclic autocorrelation matrix (CAM). This problem appears in the context of radar as well as asynchronous communications in highly non-stationary environments (e.g. telemetry) due to the Doppler effect. The parameters evolution is modelled by a zero-order random walk. The estimates are obtained from the instantaneous CAM (ICAM) of the signal. While nonlinear Kalman filter theory is a possible approach to this problem, Bayes risk theory is used instead. In this way the Riccati equation and gain matrices become independent of the estimates, thus allowing a look-up table solution. The folded normal density (FND) is assumed as parameters' prior. The prior is recursively updated in mean (estimates) and variance. By comparing the obtained equations with linear Kalman filter equations we show that with few modifications a first-order random walk model can be easily incorporated to cope with highly non-stationary frequency evolution.<>
File Size	301529
File Format	PDF
ISBN	0780317750
ISSN	15206149
DOI	10.1109/ICASSP.1994.389793
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-04-19
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Parameter estimation Riccati equations Frequency estimation Recursive estimation Computer aided manufacturing CADCAM Timing Autocorrelation Context Doppler radar
Content Type	Text
Resource Type	Article

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