Results 261 to 270 of about 9,274 (303)

Polynomial extended Kalman filter

IEEE Transactions on Automatic Control, 2005
This work presents a polynomial version of the well-known extended Kalman filter (EKF) for the state estimation of nonlinear discrete-time stochastic systems. The proposed filter, denoted polynomial EKF (PEKF), consists in the application of the optimal polynomial filter of a chosen degree mu to the Carleman approximation of a nonlinear system. When mu
GERMANI, Alfredo, MANES, COSTANZO, Palumbo P.   +2 more
openaire   +4 more sources

An interlaced extended Kalman filter

IEEE Transactions on Automatic Control, 1999
The aim of the paper is to propose an estimation algorithm, called Interlaced Extended Kalman Filter (IEKF), for a class of discrete-time nonlinear systems. The vector state of the system is assumed to be partitionable into \(m\) parts (subsystems), whose dynamic equations are supposed to be affine for each of the corresponding part (subsystem).
Luigi Glielmo, Roberto Setola, Francesco Vasca   +2 more
openaire   +2 more sources

Modified extended Kalman filtering

IEEE Transactions on Automatic Control, 1994
The authors propose a modification of the extended Kalman filtering algorithm for the following system: \[ dX_ t= f(t, X_ t) dt+ \varepsilon_ 1 \sigma_ 1 (t)dV_ t, \qquad dy_ t= h(t, X_ t) dt+ \varepsilon_ 2 \sigma_ 2 (t) dW_ t, \] where \((V_ t)\) and \((W_ t)\) are independent Brownian motions, \((X_ t)\in \mathbb{R}^ n\) and \((y_ t)\in \mathbb{R ...
Nasir U. Ahmed, S. M. Radaideh
openaire   +2 more sources

Extended Kalman filter for extended object tracking

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
In this work, we present a novel method for tracking an elliptical shape approximation of an extended object based on a varying number of spatially distributed measurements. For this purpose, an explicit nonlinear measurement equation is formulated that relates the kinematic and shape parameters to a measurement by means of a multiplicative noise term.
Shishan Yang, Marcus Baum
openaire   +1 more source

On designing consistent extended Kalman filter

Journal of Systems Science and Complexity, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yanguang Jiang, Yi Huang, Wenchao Xue 0001, Haitao Fang   +3 more
openaire   +1 more source

An extended Kalman filter for mouse tracking

Medical & Biological Engineering & Computing, 2018
Animal tracking is an important tool for observing behavior, which is useful in various research areas. Animal specimens can be tracked using dynamic models and observation models that require several types of data. Tracking mouse has several barriers due to the physical characteristics of the mouse, their unpredictable movement, and cluttered ...
Hongjun Choi, Mingi Kim, Onseok Lee
openaire   +2 more sources

Stability of distributed extended Kalman filters

2017 22nd International Conference on Digital Signal Processing (DSP), 2017
The need for faster and more robust parameter estimates in the smart grid, together with the growth in multi-sensor distributed measurements has motivated the development of distributed extended Kalman filtering (EKF) algorithms. However, fundamental theoretical insights about the convergence and stability of these distributed extended Kalman filtering
Sithan Kanna, Danilo P. Mandic
openaire   +1 more source

Bayesian filtering techniques: Kalman and extended Kalman filter basics

2009 19th International Conference Radioelektronika, 2009
Bayesian filters provide a statistical tool for dealing with measurement uncertainty. Bayesian filters estimate a state of dynamic system from noisy observations. These filters represent the state by random variable and in each time step probability distribution over random variable represents the uncertainty.
Jan Mochnac, Stanislav Marchevsky, Pavol Kocan   +2 more
openaire   +1 more source

Performance evaluation of the Extended Kalman Filter and Unscented Kalman Filter

2015 International Conference on Unmanned Aircraft Systems (ICUAS), 2015
The Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are methods usually applied in the sensor fusion for Unmanned Aerial Vehicles due to its nonlinear navigation equations. This paper presents a comparison between the two filters considering the position, velocity and attitude of the vehicle and the IMU bias.
Natassya B. F. da Silva, Daniel B. Wilson, Kalinka R. L. J. Branco   +2 more
openaire   +1 more source

Evaluation of Unscented Kalman Filter and Extended Kalman Filter for Radar Tracking Data Filtering

2014 European Modelling Symposium, 2014
This paper focuses on the issue of nonlinear data filtering in radar tracking. Through the analysis on the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), which are both nonlinear filters, we find that the accuracy of the extended Kalman filtered data image was not ideal for radar tracking data filtering, while UKF can achieve ...
Jihong Shen, Yanan Liu, Sese Wang, Zhuo Sun 0001   +3 more
openaire   +1 more source