Results 271 to 280 of about 26,219 (311)
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Neurocomputing, 2005
Anticipating future events is a crucial function of the central nervous system and can be modelled by Kalman filter-like mechanisms, which are optimal for predicting linear dynamical systems. Connectionist representation of such mechanisms with Hebbian learning rules has not yet been derived.
Gábor Szirtes +2 more
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Anticipating future events is a crucial function of the central nervous system and can be modelled by Kalman filter-like mechanisms, which are optimal for predicting linear dynamical systems. Connectionist representation of such mechanisms with Hebbian learning rules has not yet been derived.
Gábor Szirtes +2 more
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A Quadratic Kalman Filter [PDF]
SSRN Electronic Journal, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Monfort, A. +2 more
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Asian Journal of Control, 2016
AbstractIn the Extended Kalman Filter (EKF), only the first‐order term of the Taylor series is employed. Hence, the nonlinearities in the system dynamics are not fully considered. In the proposed method, to overcome this drawback, the higher‐order terms of the Taylor series are considered and a new filter, based on the Modal series, is designed.
Mohammaddadi, Gh. +2 more
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AbstractIn the Extended Kalman Filter (EKF), only the first‐order term of the Taylor series is employed. Hence, the nonlinearities in the system dynamics are not fully considered. In the proposed method, to overcome this drawback, the higher‐order terms of the Taylor series are considered and a new filter, based on the Modal series, is designed.
Mohammaddadi, Gh. +2 more
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Kalman Filtering With Intermittent Observations
IEEE Transactions on Automatic Control, 2004 Motivated by navigation and tracking applications within sensor networks, we consider the problem of performing Kalman filtering with intermittent observations. When data travel along unreliable communication channels in a large, wireless, multihop sensor network, the effect of communication delays and loss of information in the control loop cannot be ...
Bruno Sinopoli +2 more
exaly +2 more sources
International Journal of Control, 1997
For an implicitly defined discrete system, a new algorithm for Kalman filtering is developed and an efficient numerical implementation scheme is proposed. Unlike the traditional explicit approach, the implicit filter can be readily applied to ill-conditioned systems and allows for generalization to descriptor systems. The implementation of the implicit
M, Skliar, W F, Ramirez
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For an implicitly defined discrete system, a new algorithm for Kalman filtering is developed and an efficient numerical implementation scheme is proposed. Unlike the traditional explicit approach, the implicit filter can be readily applied to ill-conditioned systems and allows for generalization to descriptor systems. The implementation of the implicit
M, Skliar, W F, Ramirez
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2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), 2015
In this work, a sparse Kalman filter (SKF) exploring the signal sparse property is developed to track unknown time-varying signals. To derive SKF, the measurement update in KF is reformulated into a convex optimization problem first, and then a regularization term l 1 -norm on parameters of interest is introduced to yield sparse estimates.
Hongqing Liu +3 more
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In this work, a sparse Kalman filter (SKF) exploring the signal sparse property is developed to track unknown time-varying signals. To derive SKF, the measurement update in KF is reformulated into a convex optimization problem first, and then a regularization term l 1 -norm on parameters of interest is introduced to yield sparse estimates.
Hongqing Liu +3 more
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DIVERGENCE IN THE KALMAN FILTER
Guidance and Control Conference, 1966Under certain conditions, the orbit estimated by a Kalman filter has errors that are much greater than predicted by theory. This phenomenon is called divergence, and renders the operation of the Kalman filter unsatisfactory. This paper investigates the control of divergence in a Kalman filter used for autonomous navigation in a low earth orbit.
F. H. SCHLEE, C. J. STANDISH, N. F. TODA
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2018 IEEE/ION Position, Location and Navigation Symposium (PLANS), 2018
The Extended Kalman Filter Is a proven method for efficient Markov Chain inference. It is ubiquitous in indoor localization applications, and typically applied to combine relative motion with absolute positioning. However, an unmodified Extended Kalman Filter struggles to handle common problems in indoor applications.
Boxian Dong +2 more
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The Extended Kalman Filter Is a proven method for efficient Markov Chain inference. It is ubiquitous in indoor localization applications, and typically applied to combine relative motion with absolute positioning. However, an unmodified Extended Kalman Filter struggles to handle common problems in indoor applications.
Boxian Dong +2 more
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Annual Review of Control, Robotics, and Autonomous Systems, 2018
The Kalman filter—or, more precisely, the extended Kalman filter (EKF)—is a fundamental engineering tool that is pervasively used in control and robotics and for various estimation tasks in autonomous systems. The recently developed field of invariant extended Kalman filtering uses the geometric structure of the state space and the dynamics to improve
Bonnabel, Silvere, Barrau, Axel
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The Kalman filter—or, more precisely, the extended Kalman filter (EKF)—is a fundamental engineering tool that is pervasively used in control and robotics and for various estimation tasks in autonomous systems. The recently developed field of invariant extended Kalman filtering uses the geometric structure of the state space and the dynamics to improve
Bonnabel, Silvere, Barrau, Axel
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Information Sciences, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guanrong Chen +2 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guanrong Chen +2 more
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