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...
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| Superior document: | Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory |
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| : | |
| Year of Publication: | 2016 |
| Language: | English |
| Series: | Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory
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| Physical Description: | 1 electronic resource (XVI, 167 p. p.) |
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| 100 | 1 | |a Gilitschenski, Igor |4 auth | |
| 245 | 1 | 0 | |a Deterministic Sampling for Nonlinear Dynamic State Estimation |
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| 300 | |a 1 electronic resource (XVI, 167 p. p.) | ||
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| 490 | 1 | |a Karlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory | |
| 520 | |a 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. | ||
| 546 | |a English | ||
| 653 | |a Sensordatenfusion | ||
| 653 | |a Richtungsstatistik | ||
| 653 | |a Directional Statistics | ||
| 653 | |a Stochastische Filterung | ||
| 653 | |a Sensor Data Fusion | ||
| 653 | |a DichteapproximationStochastic Filtering | ||
| 653 | |a Density Approximation | ||
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