Deterministic Sampling for Nonlinear Dynamic State Estimation

by Igor Gilitschenski

Institution: Universit├Ąt Karlsruhe
Year: 2016
Keywords: Stochastische Filterung; Sensordatenfusion; Richtungsstatistik; DichteapproximationStochastic Filtering; Sensor Data Fusion; Directional Statistics; Density Approximation
Posted: 02/05/2017
Record ID: 2135171
Full text PDF: http://digbib.ubka.uni-karlsruhe.de/volltexte/documents/3827377


The goal of this work is improving existing and suggesting novel filtering algorithms for nonlinear dynamic state estimation. Nonlinearity is considered in two ways: First, propagation is improved by proposing novel methods for approximating continuous probability distributions by discrete distributions defined on the same continuous domain. Second, nonlinear underlying domains are considered by proposing novel filters that inherently take the underlying geometry of these domains into account.