Linear Estimation In Interconnected Sensor Systems With Information Constraints
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Linear Estimation in Interconnected Sensor Systems with Information Constraints
Author: Reinhardt, Marc
language: en
Publisher: KIT Scientific Publishing
Release Date: 2015-04-15
A ubiquitous challenge in many technical applications is to estimate an unknown state by means of data that stems from several, often heterogeneous sensor sources. In this book, information is interpreted stochastically, and techniques for the distributed processing of data are derived that minimize the error of estimates about the unknown state. Methods for the reconstruction of dependencies are proposed and novel approaches for the distributed processing of noisy data are developed.
Linear Estimation in Interconnected Sensor Systems with Information Constraints
A ubiquitous challenge in many technical applications is to estimate an unknown state by means of data that stems from several, often heterogeneous sensor sources. In this book, information is interpreted stochastically, and techniques for the distributed processing of data are derived that minimize the error of estimates about the unknown state. Methods for the reconstruction of dependencies are proposed and novel approaches for the distributed processing of noisy data are developed.
Deterministic Sampling for Nonlinear Dynamic State Estimation
Author: Gilitschenski, Igor
language: en
Publisher: KIT Scientific Publishing
Release Date: 2016-04-19
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.