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Estimating Correlated Angles Using the Hypertoroidal Grid Filter

Pfaff, Florian; Li, Kailai; Hanebeck, Uwe D.

Abstract:
Estimation for multiple correlated quantities generally requires considering a domain whose dimension is equal to the sum of the dimensions of the individual quantities. For multiple correlated angular quantities, considering a hypertoroidal manifold may be required. Based on a Cartesian product of d equidistant one-dimensional grids for the unit circle, a grid for the d-dimensional hypertorus can be constructed. This grid is used for a novel filter. For n grid points, the update step is in O(n) for arbitrary likelihoods and the prediction step is in O(n2) for arbitrary transition densities. The run time of the latter can be reduced to O(n log n) for identity models with additive noise. In an evaluation scenario, the novel filter shows faster convergence than a particle filter for hypertoroidal domains and is on par with the recently proposed Fourier filters.

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Postprint §
DOI: 10.5445/IR/1000123694
Frei zugänglich ab 01.10.2021
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 09.2020
Sprache Englisch
Identifikator ISBN: 978-172816422-9
KITopen-ID: 1000123694
Erschienen in Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020), Karlsruhe, 14 - 16 September 2020
Veranstaltung International Conference on Multisensor Fusion and Integration for Intelligent Systems (2020), Online, 14.09.2020 – 16.09.2020
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 101-107
Bemerkung zur Veröffentlichung Die Veranstaltung fand als Online-Event statt
Nachgewiesen in Dimensions
Scopus
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