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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Bibliographische Detailangaben

1. Verfasser:	Gilitschenski, Igor
Format:	Online
Sprache:	Englisch
Veröffentlicht:	KIT Scientific Publishing 2021
Schlagworte:	QA75.5-76.95 Sensordatenfusion Richtungsstatistik Directional Statistics Stochastische Filterung Sensor Data Fusion DichteapproximationStochastic Filtering Density Approximation bic Book Industry Communication::U Computing & information technology::UY Computer science
Online-Zugang:	35078
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