Deterministic Sampling for Nonlinear Dynamic State Estimation
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 distribut...
Saved in:
| Superior document: | Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory |
|---|---|
| : | |
| Year of Publication: | 2016 |
| Language: | English |
| Series: | Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory
|
| Physical Description: | 1 electronic resource (XVI, 167 p. p.) |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISBN: | 1000051670 |
| Hierarchical level: | Monograph |