Explore
Deterministic Sampling for Nonlinear Dynamic State Estimation
Igor Gilitschenski
2016
0 Ungluers have
Faved this Work
Login to Fave
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.
This book is included in DOAB.
Why read this book? Have your say.
You must be logged in to comment.