Practical development of the second-order extended Kalman filter for very long range radar tracking

2011 ◽  
Vol 91 (5) ◽  
pp. 1240-1248 ◽  
Author(s):  
Wei Mei ◽  
Ganlin Shan ◽  
Chunping Wang
Keyword(s):  
Kalman Filter ◽  
Long Range ◽  
Extended Kalman Filter ◽  
Second Order ◽  
Radar Tracking ◽  
Practical Development

scholarly journals Leakage Detection in Pipeline Based on Second Order Extended Kalman Filter Observer

2019 ◽  
Vol 52 (29) ◽  
pp. 116-121 ◽  
Author(s):  
S. Razvarz ◽  
R. Jafari ◽  
C. Vargas-Jarillo ◽  
A. Gegov ◽  
M. Forooshani
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Second Order ◽  
Leakage Detection

Dynamic Parameter Estimation of a Nonlinear Vessel Steering Model for Ocean Navigation

2011 ◽  
Author(s):  
Lokukaluge P. Perera ◽  
Paulo Oliveira ◽  
C. Guedes Soares
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Dynamic Parameter ◽  
Second Order ◽  
Stochastic Parameters ◽  
Dynamic Parameter Estimation ◽  
Nomoto Model ◽  
Numerical Simulator

In this paper the stochastic parameters describing the nonlinear ocean vessel steering model are identified, resorting to an Extended Kalman Filter. The proposed method is applied to a second order modified Nomoto model for the vessel navigation that is derived from first physics principles. The results obtained resorting to a realistic numerical simulator for the nonlinear vessel steering model considered are illustrated in this study.

scholarly journals Comparison of Backpropagation and Kalman Filter-based Training for Neural Networks

2021 ◽  
Author(s):  
Laurin Luttmann ◽  
Paolo Mercorelli
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Simulated Data ◽  
Second Order ◽  
Classification Task ◽  
Training Method ◽  
Speed Of Convergence ◽  
Backpropagation Algorithm

This work describes and compares the backpropagation algorithm with the Extended Kalman filter, a second-order training method which can be applied to the problem of learning neural network parameters and is known to converge in only a few iterations. The algorithms are compared with respect to their effectiveness and speed of convergence using simulated data for both, a regression and a classification task.

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