NDLI: Continuous-time AR model identification: Does sampling rate really matter?

Content Provider	IEEE Xplore Digital Library
Author	Maggio, S. Kirshner, H. Unser, M.
Copyright Year	2010
Description	Author affiliation: Dept. of Electron., Comput. Sci. & Syst. (DEIS), Univ. of Bologna, Bologna, Italy (Maggio, S.) \|\| Biomedicai Imaging Group, Ecole Polytech. Fed. de Lausanne (EPFL), Lausanne, Switzerland (Kirshner, H.; Unser, M.)
Abstract	We address the problem of identifying continuous-time auto regressive (CAR) models from sampled data. The exponential nature of CAR autocorrelation functions is taken into account by means of exponential B-splines modelling, allowing one to associate the available digital data with a CAR model. A maximum likelihood (ML) estimator is then derived for identifying the optimal parameters; it relies on an exact discretization of the sampled version of the continuous-time model. We provide both time- and frequency-domain interpretations of the proposed estimator, while introducing a weighting function that describes the CAR power spectrum by means of discrete Fourier transform values. We present experimental results demonstrating that the proposed exponential-based ML estimator outperforms currently available polynomial-based methods, while achieving Cramér-Rao lower bound values even for relatively low sampling rates.
Starting Page	1469
Ending Page	1473
File Size	289491
Page Count	5
File Format	PDF
ISSN	22195491
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-23
Publisher Place	Denmark
Access Restriction	Subscribed
Rights Holder	EUSIPCO
Subject Keyword	Band-pass filters Maximum likelihood estimation Discrete Fourier transforms Cost function Polynomials Data models Splines (mathematics)
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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