Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications
By restricting to Gaussian distributions, the optimal Bayesian filtering problem can be transformed into an algebraically simple form, which allows for computationally efficient algorithms. Three problem settings are discussed in this thesis: (1) filtering with Gaussians only, (2) Gaussian mixture f...
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic Book Chapter |
| Language: | English |
| Published: |
KIT Scientific Publishing
2015
|
| Series: | Karlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe
|
| Subjects: | |
| Online Access: | DOAB: download the publication DOAB: description of the publication |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
MARC
| LEADER | 00000naaaa2200000uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_54758 | ||
| 005 | 20210211 | ||
| 003 | oapen | ||
| 006 | m o d | ||
| 007 | cr|mn|---annan | ||
| 008 | 20210211s2015 xx |||||o ||| 0|eng d | ||
| 020 | |a KSP/1000045491 | ||
| 020 | |a 9783731503385 | ||
| 040 | |a oapen |c oapen | ||
| 024 | 7 | |a 10.5445/KSP/1000045491 |c doi | |
| 041 | 0 | |a eng | |
| 042 | |a dc | ||
| 100 | 1 | |a Huber, Marco |4 auth | |
| 245 | 1 | 0 | |a Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications |
| 260 | |b KIT Scientific Publishing |c 2015 | ||
| 300 | |a 1 electronic resource (V, 270 p. p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Karlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe | |
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a By restricting to Gaussian distributions, the optimal Bayesian filtering problem can be transformed into an algebraically simple form, which allows for computationally efficient algorithms. Three problem settings are discussed in this thesis: (1) filtering with Gaussians only, (2) Gaussian mixture filtering for strong nonlinearities, (3) Gaussian process filtering for purely data-driven scenarios. For each setting, efficient algorithms are derived and applied to real-world problems. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by-sa/4.0/ |2 cc |4 https://creativecommons.org/licenses/by-sa/4.0/ | ||
| 546 | |a English | ||
| 653 | |a Zustandsschätzung | ||
| 653 | |a GaußprozesseBayesian statistics | ||
| 653 | |a Kalman filter | ||
| 653 | |a Gaussian processes | ||
| 653 | |a Kalman-Filter | ||
| 653 | |a state estimation | ||
| 653 | |a filtering | ||
| 653 | |a Bayes'sche Statistik | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://www.ksp.kit.edu/9783731503385 |7 0 |z DOAB: download the publication |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/54758 |7 0 |z DOAB: description of the publication |