Results 221 to 230 of about 12,338 (264)
Performance enhancement of diffuse fluorescence tomography based on an extended Kalman filtering-long short term memory neural network correction model. [PDF]
Biomed Opt ExpressXing L +5 more
europepmc +1 more source
Correction: Bayesian inference of kinetic schemes for ion channels by Kalman filtering.
ElifeMünch JL +3 more
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Nonlinear filtering with exogenous Kalman filter and double Kalman filter
2016 European Control Conference (ECC), 2016We propose two variants of the Linearized Kalman Filter (LKF). The model linearization is made about an auxiliary state estimate that can be seen as an exogenous input to the LFK, and the resulting two-stage estimator is called an exogenous Kalman filter (XKF).
Tor Arne Johansen, Thor I. Fossen
openaire +1 more source
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
openaire +1 more source
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
openaire +1 more source
A Quadratic Kalman Filter [PDF]
SSRN Electronic Journal, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Monfort, A. +2 more
openaire +1 more source
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
openaire +1 more source
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
openaire +1 more source
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
openaire +1 more source
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
openaire +2 more sources
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
openaire +2 more sources
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
openaire +1 more source
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
openaire +1 more source
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
openaire +1 more source
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
openaire +1 more source