Název:	Copula-based convolution for fast point-mass prediction
Autoři:	Duník, Jindřich Straka, Ondřej Matoušek, Jakub Blasch, Erik
Citace zdrojového dokumentu:	DUNÍK, J. STRAKA, O. MATOUŠEK, J. BLASCH, E. Copula-based convolution for fast point-mass prediction. Signal Processing, 2022, roč. 192, č. March 2022, s. 1-10. ISSN: 0165-1684
Datum vydání:	2022
Nakladatel:	Elsevier
Typ dokumentu:	článek article
URI:	2-s2.0-85118532393 http://hdl.handle.net/11025/47015
ISSN:	0165-1684
Klíčová slova v dalším jazyce:	state estimation, nonlinear systems, Bayesian relations, convolution, point-mass filter, copula
Abstrakt v dalším jazyce:	This paper deals with the state estimation of the nonlinear stochastic dynamic discrete-in-time models by a numerical solution to the Bayesian recursive relations represented by the point-mass filter (PMF). In particular, emphasis is placed on the development of the fast convolution, which reduces computational complexity of the PMF prediction step by the orders of magnitude for models with a diagonal form of the dynamic equation. The copula-based convolution decomposes the joint conditional density into the marginal densities (allowing efficient prediction) and an easy-to-calculate copula density function. As a consequence, it has the linear growth of its computational complexity with the state dimension, which is in a contrast with the exponential growth of the standard convolution complexity in PMF methods. The proposed fast convolution is analysed and illustrated in a numerical study for a static example and a dynamic terrain-aided navigation scenario. An exemplary implementation of the proposed convolution is provided along with the paper.
Práva:	Plný text je přístupný v rámci univerzity přihlášeným uživatelům. © Elsevier
Vyskytuje se v kolekcích:	Články / Articles (KKY) OBD

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