文章基本信息

标题：Hyperspherical Unscented Particle Filter for Nonlinear Orientation Estimation
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作者：Kailai Li ; Florian Pfaff ; Uwe D. Hanebeck 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：2347-2353
DOI：10.1016/j.ifacol.2020.12.030
语种：English
出版社：Elsevier
摘要：AbstractWe propose a novel quaternion particle filter for nonlinear SO(3) estimation. For importance sampling, the proposal distribution is designed to incorporate newly observed evidence. For that, the unscented Kalman filtering is performed particle-wise on the tangent plane of the unit quaternion manifold via gnomonic projection/retraction based on hyperspherical geometry. As prior particles are driven towards high-likelihood regions on the manifold, computational efficiency of quaternion particle filtering is significantly improved. The resulting hyperspherical unscented particle filter (HUPF) is evaluated for nonlinear orientation estimation in simulations. Results show that it gives superior tracking performance compared with the conventional particle filter and other existing quaternion filtering schemes relying on parametric modeling.
关键词：KeywordsParameter estimationinformation fusionrecursive Bayesian filteringdirectional estimationunscented particle filterhyperspherical geometry