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.
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TL;DR: In this article, the Kalman Filter was used for forecasting, structural time series and Kalman filter was applied to the Structural Time Series (STS) in the context of time series forecasting.
Abstract: (1992). Forecasting, Structural Time Series and the Kalman Filter. Technometrics: Vol. 34, No. 4, pp. 496-497.
300 citations
TL;DR: In this article, the authors analyzed the convergence aspects of the invariant extended Kalman filter (IEKF) when the latter is used as a deterministic nonlinear observer on Lie groups, for continuous-time systems with discrete observations.
Abstract: We analyze the convergence aspects of the invariant extended Kalman filter (IEKF), when the latter is used as a deterministic nonlinear observer on Lie groups, for continuous-time systems with discrete observations. One of the main features of invariant observers for left-invariant systems on Lie groups is that the estimation error is autonomous. In this paper we first generalize this result by characterizing the (much broader) class of systems for which this property holds. For those systems, the Lie logarithm of the error turns out to obey a linear differential equation. Then, we leverage this “log-linear” property of the error evolution, to prove for those systems the local stability of the IEKF around any trajectory, under the standard conditions of the linear case. One mobile robotics example and one inertial navigation example illustrate the interest of the approach. Simulations evidence the fact that the EKF is capable of diverging in some challenging situations, where the IEKF with identical tuning keeps converging.
292 citations
TL;DR: In this paper, a simple and intuitive derivation of the Kalman filter is presented, with the aim of teaching this useful tool to students from disciplines that do not require a strong mathematical background.
Abstract: This article provides a simple and intuitive derivation of the Kalman filter, with the aim of teaching this useful tool to students from disciplines that do not require a strong mathematical background. The most complicated level of mathematics required to understand this derivation is the ability to multiply two Gaussian functions together and reduce the result to a compact form.
288 citations
TL;DR: In this article, a modified gain extended Kalman observer (MGEKO) was developed for a special class of systems and a sufficient condition for the estimation errors of the MGEKF to be exponentially bounded in the mean square was obtained.
Abstract: A new globally convergent nonlinear observer, called the modified gain extended Kalman observer (MGEKO), is developed for a special class of systems. This observer structure forms the basis of a new stochastic filter mechanization called the modified gain extended Kalman filter (MGEKF). A sufficient condition for the estimation errors of the MGEKF to be exponentially bounded in the mean square is obtained. Finally, the MGEKO and the MGEKF are applied to the three-dimensional bearings-only measurement problem where the extended Kalman filter often shows erratic behavior.
287 citations
TL;DR: In this paper, the extended Kalman filter for a multilayer nonlinear quasi-geostrophic ocean circulation model is discussed, where the transition matrix can be split into two parts, where one part results in pure evolution of error covariances in the model velocity field, and the other part contains a statistical correction term caused by the nonlinearity in a model.
Abstract: The formulation of the extended Kalman filter for a multilayer nonlinear quasi-geostrophic ocean circulation model is discussed. The nonlinearity in the ocean model leads to an approximative equation for error covariance propagation, where the transition matrix is dependent on the state trajectory. This nonlinearity complicates the dynamics of the error covariance propagation, and effects which are nonexistent in linear systems contribute significantly. The transition matrix can be split into two parts, where one part results in pure evolution of error covariances in the model velocity field, and the other part contains a statistical correction term caused by the nonlinearity in the model. This correction term leads to a linear unbounded instability, which is caused by the statistical linearization of the nonlinear error propagation equation. Different ways of handling this instability are discussed. Further, nonlinear small-scale instabilities also develop, since energy is accumulated at wavelengths 2Δx, owing to the numerical discretization. These small-scale oscillations are removed with a Shapiro filter, and the effect they have on the error covariance propagation is discussed. Some data assimilation experiments are performed using the full extended Kalman filter, to examine the properties of the filter. An experiment where only the first part of the transition matrix is used to propagate the error covariances is also performed. This simplified experiment actually performs better than the full extended Kalman filter because the unbounded instability associated with the statistical correction term is avoided.
287 citations