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Progressive von mises-fisher filtering using isotropic sample sets for nonlinear hyperspherical estimation.
Sensors 21:2991 (2021)
In this work, we present a novel scheme for nonlinear hyperspherical estimation using the von Mises-Fisher distribution. Deterministic sample sets with an isotropic layout are exploited for the efficient and informative representation of the underlying distribution in a geometrically adaptive manner. The proposed deterministic sampling approach allows manually configurable sample sizes, considerably enhancing the filtering performance under strong nonlinearity. Furthermore, the progressive paradigm is applied to the fusing of measurements of non-identity models in conjunction with the isotropic sample sets. We evaluate the proposed filtering scheme in a nonlinear spherical tracking scenario based on simulations. Numerical results show the evidently superior performance of the proposed scheme over state-of-the-art von Mises-Fisher filters and the particle filter.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Sensor Fusion ; Recursive Bayesian Estimation ; Directional Statistics ; Unscented Transform ; Nonlinear Hyperspherical Filtering; Multivariate; Simulation
Language
english
Publication Year
2021
HGF-reported in Year
2021
ISSN (print) / ISBN
1424-8220
e-ISSN
1424-8220
Journal
Sensors
Quellenangaben
Volume: 21,
Issue: 9,
Article Number: 2991
Publisher
MDPI
Publishing Place
St Alban-anlage 66, Ch-4052 Basel, Switzerland
Reviewing status
Peer reviewed
Institute(s)
Helmholtz AI - KIT (HAI - KIT)
Grants
Helmholtz AI Cooperation Unit within the scope of the project "Ubiquitous Spatio-Temporal Learning for Future Mobility"(ULearn4Mobility)
WOS ID
WOS:000650762200001
Erfassungsdatum
2021-06-30