Particle filtering with alpha-stable distributions
Mihaylova, L. and Brasnett, P. and Achim, A. and Bull, D. and Canagarajah, N.
(2005)
Particle filtering with alpha-stable distributions.
In:
Statistical Signal Processing, 2005 IEEE/SP 13th Workshop on :.
UNSPECIFIED, Bordeaux, France, 381 - 386.
ISBN 0-7803-9403-8
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Abstract
In this paper we introduce a novel sequential Monte Carlo technique, which is based on the family of symmetric alpha- stable (SAS) distributions. Sequential Bayesian estimation generally involves recursive estimation of filtering and predictive distributions of unobserved signals from their noisy measurements. In our proposed algorithm, the relevant density functions are approximated by particles drawn from stable distributions. We call this novel technique SAS particle filtering (SASPF). We assess the performance of the SASPF in comparison with the Gaussian Sum particle filter (GSPF) [1] and a standard (non-parametric) particle filter (PF). Results obtained using highly nonlinear models with simulated data show that the SASPF outperforms the GSPF and compares very favorably with the PF.
Item Type:
Contribution in Book/Report/Proceedings
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Uncontrolled Keywords:
/dk/atira/pure/researchoutput/libraryofcongress/qa75
Subjects:
?? alpha stable distributionsparticle filteringestimationnonlinear systems dcs-publications-idinproc-431dcs-publications-creditsdsp-fadcs-publications-personnel-id121qa75 electronic computers. computer science ??
Deposited On:
10 Mar 2008 13:37
Last Modified:
23 Dec 2024 01:22