Gilitschenski, Igor <br/><br/>
Deterministic Sampling for Nonlinear Dynamic State Estimation 

 -  KIT Scientific Publishing  2016 
 - 1 electronic resource (XVI, 167 p. p.) 
<br/><br/> Open Access 
<br/><br/> 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. 
<br/><br/><br/>Creative Commons 
<br/><br/><br/>English 
<br/><br/><label>ISBN: </label>KSP/1000051670 9783731504733 
<br/><br/><label>Standard No.: </label>10.5445/KSP/1000051670  doi 
<br/><br/><label>Subjects--Index Terms: </label>Sensordatenfusion Richtungsstatistik Directional Statistics Stochastische Filterung Sensor Data Fusion DichteapproximationStochastic Filtering Density Approximation