Title:	Point-Mass Filter with Decomposition of Transient Density
Authors:	Tichavský, Petr Straka, Ondřej Duník, Jindřich
Citation:	TICHAVSKÝ, P. STRAKA, O. DUNÍK, J. Point-Mass Filter with Decomposition of Transient Density. In Proceedings of the 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Singapore: IEEE, 2022. s. 5752-5756. ISBN: 978-1-66540-540-9 , ISSN: 1520-6149
Issue Date:	2022
Publisher:	IEEE
Document type:	konferenční příspěvek ConferenceObject
URI:	2-s2.0-85131241649 http://hdl.handle.net/11025/51458
ISBN:	978-1-66540-540-9
ISSN:	1520-6149
Keywords in different language:	State estimation;filtering, nonlinear systems;point-mass method;non-negative matrix factorization
Abstract in different language:	The paper deals with the state estimation of nonlinear stochastic dynamic systems with special attention on a grid-based numerical solution to the Bayesian recursive relations, the point-mass filter (PMF). In the paper, a novel functional decomposition of the transient density describing the system dynamics is proposed. The decomposition is based on a non-negative matrix factorization and separates the density into functions of the future and current states. Such decomposition facilitates a thrifty calculation of the convolution, which is a bottleneck of the PMF performance. The PMF estimate quality and computational costs can be efficiently controlled by choosing an appropriate rank of the decomposition. The performance of the PMF with the transient density decomposition is illustrated in a terrain-aided navigation scenario.
Rights:	Plný text je přístupný v rámci univerzity přihlášeným uživatelům. © IEEE
Appears in Collections:	Konferenční příspěvky / Conference papers (NTIS) Konferenční příspěvky / Conference Papers (KKY) OBD

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