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Deterministic Sampling for Nonlinear Dynamic State Estimation
by Igor Gilitschenski
Institution: | Universität Karlsruhe |
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Department: | |
Degree: | |
Year: | 2016 |
Keywords: | Stochastische Filterung; Sensordatenfusion; Richtungsstatistik; DichteapproximationStochastic Filtering; Sensor Data Fusion; Directional Statistics; Density Approximation |
Posted: | 2/5/2017 12:00:00 AM |
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.
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