Unified Linearization-based Nonlinear Filtering

Autor:	Kullberg, Anton, Skog, Isaac, Hendeby, Gustaf
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	This letter shows that the following three classes of recursive state estimation filters: standard filters, such as the extended Kalman filter; iterated filters, such as the iterated unscented Kalman filter; and dynamically iterated filters, such as the dynamically iterated posterior linearization filters; can be unified in terms of a general algorithm. The general algorithm highlights the strong similarities between specific filtering algorithms in the three filter classes and facilitates an in-depth understanding of the pros and cons of the different filter classes and algorithms. We end with a numerical example showing the estimation accuracy differences between the three classes of filters when applied to a nonlinear localization problem. Comment: 4 pages, 1 page reference. arXiv admin note: text overlap with arXiv:2302.13871
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2309.07631 Zobrazit plný text záznamu View this record from Arxiv