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
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TL;DR: In this article, a large number of ad hoc modifications are required to prevent divergence, resulting in a rather complex filter and performance is quite good as judged by comparison of Monte Carlo simulations with the Cramer-Rao lower bound, and by the filters ability to track maneuvering targets.
Abstract: It is well known that the extended Kalman filtering methodology works well in situations characterized by a high signal-to-noise ratio, good observability and a valid state trajectory for linearization. This paper considers a problem not characterized by these favorable conditions. A large number of ad hoc modifications are required to prevent divergence, resulting in a rather complex filter. However, performance is quite good as judged by comparison of Monte Carlo simulations with the Cramer-Rao lower bound, and by the filters ability to track maneuvering targets.
78 citations
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TL;DR: In this paper, an extension to the particle filtering toolbox that enables nonlinear/non-Gaussian filtering to be performed in the presence of out-of-sequence measurements (OOSMs) with arbitrary lag is presented.
Abstract: An extension is presented to the particle filtering toolbox that enables nonlinear/non-Gaussian filtering to be performed in the presence of out-of-sequence measurements (OOSMs) with arbitrary lag, without the need to adopt linearising approximations in the filter and without the degradation of performance that would occur if the OOSMs were simply discarded. An estimate of the performance of the OOSM particle filter (OOSM-PF) is obtained for bearings-only tracking scenarios with a single target and a small number of sensors. These performance estimates are then compared with the posterior Cramer-Rao lower bound (CRLB) for the state estimate rms error and similar performance estimates obtained from the oosm extended Kalman filter (OOSM-EKF) algorithms recently introduced in the literature. For a mildly nonlinear bearings-only tracking problem the OOSM-PF and OOSM-EKF are shown to achieve broadly similar performance.
78 citations
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31 Aug 2012TL;DR: The Kalman filter arose out of R.E. Kalman's interest in applying the concept of state vectors to the Wiener filtering problem, and quickly became an essential component of modern control systems theory and practice.
Abstract: The Kalman filter arose out of R.E. Kalman's interest in applying the concept of state vectors to the Wiener filtering problem. The success of this method was evident in early applications to trajectory estimation and control of spacecraft; it was so successful, in fact, that the Kalman filter quickly became an essential component of modern control systems theory and practice. This initial success led to the propagation of Kalman-filtering ideas to other scientific disciplines, within which the methodology was adapted to suit numerous state–space oriented problems. Kalman filtering (of spatial data) is still an active area of research in the atmospheric and oceanic sciences.
78 citations
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TL;DR: It is shown that, in this kind of sensor fusion problem, the particle filter outperforms the extended Kalman filter, at the cost of more demanding computations.
Abstract: State estimation is a major problem in industrial systems, particularly in industrial robotics. To this end, Gaussian and nonparametric filters have been developed. In this paper, the extended Kalman filter, which assumes Gaussian measurement noise, is compared with the particle filter, which does not make any assumption on the measurement noise distribution. As a case study, the estimation of the state vector of an industrial robot is used when measurements are available from an accelerometer that was mounted on the end effector of the robotic manipulator and from the encoders of the joints' motors. It is shown that, in this kind of sensor fusion problem, the particle filter outperforms the extended Kalman filter, at the cost of more demanding computations.
78 citations
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01 May 1995TL;DR: In this article, two algorithms are proposed for dynamic state estimation which incorporate the measurement function nonlinearities in the extended Kalman filter (EKF) scheme, and the performance of the schemes are compared with the standard linear EKF scheme under various conditions.
Abstract: Dynamic state estimation in power systems is based on the extended Kalman filter (EKF) scheme The EKF system uses a linearised measurement equation, neglecting the nonlinearities of the measurement function Under certain circumstances (eg large load changes) this leads to degradation in the filter performance Two algorithms are proposed for dynamic state estimation which incorporate the measurement function nonlinearities in the EKF scheme The performance of the schemes are compared with the standard linear EKF scheme under various conditions and comparative results are presented
78 citations