Topic
Invariant extended Kalman filter
About: Invariant extended Kalman filter is a research topic. Over the lifetime, 7079 publications have been published within this topic receiving 187702 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: In this article, an invariant nonlinear observer (i.e., a filter) is proposed for estimating the velocity vector and orientation of a flying rigid body, using measurements from low-cost Earth-fixed velocity, inertial and magnetic sensors.
73 citations
TL;DR: A Robust Kalman filtering method is proposed for the attitude estimation problem and two new algorithms, which are robust against measurement malfunctions, are called Robust Extended Kalman Filter and Robust Unscented Kalman filter, respectively.
73 citations
TL;DR: This paper reviews Bayesian filters that possess the aforementioned properties, and focuses on parametric methods, among which there are three types of filters: filters based on analytical approximations (extended Kalman filter, iterated extended Kalman filters, and Gauss-Hermite filter), and filtersbased on the Gaussian sum approximation (Gaussian sum filter).
Abstract: Nonlinear stochastic dynamical systems are commonly used to model physical processes. For linear and Gaussian systems, the Kalman filter is optimal in minimum mean squared error sense. However, for nonlinear or non-Gaussian systems, the estimation of states or parameters is a challenging problem. Furthermore, it is often required to process data online. Therefore, apart from being accurate, the feasible estimation algorithm also needs to be fast. In this paper, we review Bayesian filters that possess the aforementioned properties. Each filter is presented in an easy way to implement algorithmic form. We focus on parametric methods, among which we distinguish three types of filters: filters based on analytical approximations (extended Kalman filter, iterated extended Kalman filter), filters based on statistical approximations (unscented Kalman filter, central difference filter, Gauss-Hermite filter), and filters based on the Gaussian sum approximation (Gaussian sum filter). We discuss each of these filters, and compare them with illustrative examples.
73 citations
TL;DR: This paper aims at reducing the noise using the Kalman filter by building an image model based on Markov random field and introducing a multi-innovation to improve the filtering/smoothing performance.
73 citations
DOI•
01 May 1980
TL;DR: In this article, the Kalman filter is used to remove the wave motion signals from a dynamically positioned vessel to ensure that the sytem only responds to low-frequency forces that would cause the vessel to move off-station.
Abstract: The position-control systems for dynamically positioned vessels include wave filters to remove the wave motion signals. These ensure that the sytem only responds to low-frequency forces that would cause the vessel to move off-station. Several filters have been proposed and used in this role, and in the following discussion the Kalman filter is considered. The Kalman filter depends upon the model of the vessel, and the development of such a model is described. Simulation results are given to illustrate the performance of the filter and the performance of the combined Kalman filter and optimal state-feedfack control system.
72 citations