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A New Extension of the Kalman Filter to Nonlinear Systems

by Simon J. Julier, Jeffrey K. Uhlmann , 1997
"... The Kalman filter(KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be difficult. The most common approach is to use the Extended Kalman Filter (EKF) which ..."
Abstract - Cited by 778 (6 self) - Add to MetaCart
) which simply linearises all nonlinear models so that the traditional linear Kalman filter can be applied. Although the EKF (in its many forms) is a widely used filtering strategy, over thirty years of experience with it has led to a general consensus within the tracking and control community

Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics

by Geir Evensen - J. Geophys. Res , 1994
"... . A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter. The ..."
Abstract - Cited by 800 (23 self) - Add to MetaCart
. The unbounded error growth found in the extended Kalman filter, which is caused by an overly simplified closure in the error covariance equation, is completely eliminated. Open boundaries can be handled as long as the ocean model is well posed. Well-known numerical instabilities associated with the error

A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking

by M. Sanjeev Arulampalam, Simon Maskell, Neil Gordon - IEEE TRANSACTIONS ON SIGNAL PROCESSING , 2002
"... Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view o ..."
Abstract - Cited by 2006 (2 self) - Add to MetaCart
Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view

The Infinite Hidden Markov Model

by Matthew J. Beal, Zoubin Ghahramani, Carl E. Rasmussen - Machine Learning , 2002
"... We show that it is possible to extend hidden Markov models to have a countably infinite number of hidden states. By using the theory of Dirichlet processes we can implicitly integrate out the infinitely many transition parameters, leaving only three hyperparameters which can be learned from data. Th ..."
Abstract - Cited by 637 (41 self) - Add to MetaCart
We show that it is possible to extend hidden Markov models to have a countably infinite number of hidden states. By using the theory of Dirichlet processes we can implicitly integrate out the infinitely many transition parameters, leaving only three hyperparameters which can be learned from data

Link-Sharing and Resource Management Models for Packet Networks

by Sally Floyd, Van Jacobson , 1995
"... This paper discusses the use of link-sharing mechanisms in packet networks and presents algorithms for hierarchical link-sharing. Hierarchical link-sharing allows multiple agencies, protocol families, or traflic types to share the bandwidth on a tink in a controlled fashion. Link-sharing and real-t ..."
Abstract - Cited by 618 (12 self) - Add to MetaCart
This paper discusses the use of link-sharing mechanisms in packet networks and presents algorithms for hierarchical link-sharing. Hierarchical link-sharing allows multiple agencies, protocol families, or traflic types to share the bandwidth on a tink in a controlled fashion. Link-sharing and real

Social force model for pedestrian dynamics

by Dirk Helbing, Péter Molnár - Physical Review E , 1995
"... It is suggested that the motion of pedestrians can be described as if they would be subject to ‘social forces’. These ‘forces ’ are not directly exerted by the pedestrians ’ personal environment, but they are a measure for the internal motivations of the individuals to perform certain actions (movem ..."
Abstract - Cited by 504 (25 self) - Add to MetaCart
, terms reflecting that a pedestrian keeps a certain distance to other pedestrians and borders. Third, a term modeling attractive effects. The resulting equations of motion are nonlinearly coupled Langevin equations. Computer simulations of crowds of interacting pedestrians show that the social force

Constrained model predictive control: Stability and optimality

by D. Q. Mayne, J. B. Rawlings, C. V. Rao, P. O. M. Scokaert - AUTOMATICA , 2000
"... Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon open-loop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence and t ..."
Abstract - Cited by 738 (16 self) - Add to MetaCart
important because efficiency demands operating points on or close to the boundary of the set of admissible states and controls. In this review, we focus on model predictive control of constrained systems, both linear and nonlinear and discuss only briefly model predictive control of unconstrained nonlinear

A discriminatively trained, multiscale, deformable part model

by Pedro Felzenszwalb, David Mcallester, Deva Ramanan - In IEEE Conference on Computer Vision and Pattern Recognition (CVPR-2008 , 2008
"... This paper describes a discriminatively trained, multiscale, deformable part model for object detection. Our system achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge. It also outperforms the best results in the 2007 challenge ..."
Abstract - Cited by 555 (11 self) - Add to MetaCart
is specified for the positive examples. We believe that our training methods will eventually make possible the effective use of more latent information such as hierarchical (grammar) models and models involving latent three dimensional pose. 1.

Initial Conditions and Moment Restrictions in Dynamic Panel Data Models

by Richard Blundell, Stephen Bond - Journal of Econometrics , 1998
"... Estimation of the dynamic error components model is considered using two alternative linear estimators that are designed to improve the properties of the standard firstdifferenced GMM estimator. Both estimators require restrictions on the initial conditions process. Asymptotic efficiency comparisons ..."
Abstract - Cited by 2393 (16 self) - Add to MetaCart
comparisons and Monte Carlo simulations for the simple AR(1) model demonstrate the dramatic improvement in performance of the proposed estimators compared to the usual first-differenced GMM estimator, and compared to non-linear GMM. The importance of these results is illustrated in an application

Impulses and Physiological States in Theoretical Models of Nerve Membrane

by Richard Fitzhugh - Biophysical Journal , 1961
"... ABSTRACT Van der Pol's equation for a relaxation oscillator is generalized by the addition of terms to produce a pair of non-linear differential equations with either a stable singular point or a limit cycle. The resulting "BVP model " has two variables of state, representing excitabi ..."
Abstract - Cited by 505 (0 self) - Add to MetaCart
ABSTRACT Van der Pol's equation for a relaxation oscillator is generalized by the addition of terms to produce a pair of non-linear differential equations with either a stable singular point or a limit cycle. The resulting "BVP model " has two variables of state, representing
Results 11 - 20 of 71,949