0559994 20230316105347.420220815235959.9 85131241649 000864187906010 10.1109/ICASSP43922.2022.9747607 5 s. P cav_un_epca*0559993978-1-6654-0540-95752-5756SingaporeIEEE2022 State estimation nonlinear systems point-mass method cav_un_auth*0101212 Tichavský Petr UTIA-B Stochastická informatika Department of Stochastic Informatics SI SI Department of Stochastic Informatics 50 Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0434606 Straka O. CZ 40 cav_un_auth*0213309 Duník J. CZ 10 http://library.utia.cas.cz/separaty/2022/SI/tichavsky-0559994.pdf GA22-11101S GA ČR CZ cav_un_auth*0435406 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. cav_un_auth*0434607 IEEE International Conference on Acoustics, Speech, and Signal Processing 2022 20220522 20220527 Singapur SG BB 20000 20200 20205 2023 3 PR RVO:67985556 https://hdl.handle.net/11104/0333427 S n.a. Proceedings Paper Acoustics|Computer Science Artificial Intelligence|Engineering Electrical Electronic 2022 85131241649 SCOPUS PUBMED 000864187906010 WOS cav_un_epca*0559993 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing IEEE 2022 Singapore 5752 5756 978-1-6654-0540-9