Estimate of the dynamical error of a vitreous magnetooptical sensor

1989 ◽  
Vol 32 (12) ◽  
pp. 1177-1180
Author(s):  
A. V. Bogolyubov
Keyword(s):  
Dynamical Error

Research on Seamless INS/GPS Integrated Navigation Algorithm

2011 ◽  
Vol 299-300 ◽  
pp. 1178-1181
Author(s):  
Tian Lai Xu ◽  
Yang Tian
Keyword(s):  
Neural Network ◽  
Estimation Accuracy ◽  
Gps Data ◽  
Positioning Error ◽  
Integrated Navigation ◽  
Adaptive Kalman Filter ◽  
Integrated Navigation System ◽  
Navigation Algorithm ◽  
Dynamical Error ◽  
Navigation Method

This paper proposed and discussed an INS/GPS integrated navigation method based on radial basis function neural network (RBFNN) to fuse INS and GPS data. When GPS signals were available, an adaptive Kalman filter was used to improve the estimation accuracy of INS errors, and then the RBFNN structure was trained to mimic the dynamical error model of INS. If GPS signals were unavailable, the trained RBFNN structure was utilized to bridge the GPS outages to achieve seamless navigation. Simulations in INS/GPS integrated navigation system showed the proposed method can reduce the positioning error during GPS outages.

ESTIMATING ERROR GROWTH AND SHADOW BEHAVIOR IN NONLINEAR DYNAMICAL SYSTEMS

2005 ◽  
Vol 15 (10) ◽  
pp. 3265-3280 ◽  
Author(s):  
D. ORRELL
Keyword(s):  
Dynamical Systems ◽  
Nonlinear Dynamical Systems ◽  
Large Set ◽  
Error Growth ◽  
Model Quality ◽  
Nonlinear Dynamical ◽  
Dynamical Error ◽  
Error Dynamics ◽  
System Errors ◽  
True System

In modeling nonlinear dynamical systems, error growth can arise both because of dynamical error in the model, and observational error in measurements of the underlying system. Errors thus introduced can be further amplified by the system dynamics. This paper develops a method to approximate error growth, based on the model drift, for the general case when the model is given by a (possibly large) set of ordinary differential equations. The ability of the model to shadow (stay close to) the true system is analyzed, and a criterion for model quality based on the error dynamics is proposed. Examples are given with a range of models, which are chosen to be representative of different types of model error.

A Secure Communications System Using Chaotic Switching

1997 ◽  
Vol 07 (06) ◽  
pp. 1383-1394 ◽  
Author(s):  
Chungyong Lee ◽  
Douglas B. Williams ◽  
Jaejin Lee
Keyword(s):  
System Dynamics ◽  
Chaotic Systems ◽  
Reduction Method ◽  
Initial Conditions ◽  
Power Level ◽  
The Self ◽  
Secure Communications ◽  
Dynamical Error ◽  
Communications System ◽  
Very High

Since previous secure communications methods using chaotic systems require very high SNRs, knowledge of the initial conditions, and/or chaotic systems with the self-synchronization property, their application is limited. In this paper, a new secure communications scheme based on chaotic switching is discussed. The proposed scheme makes decisions on the incoming bits by comparing the power level of the dynamical error for each symbol signal after applying a noise reduction method based on the known system dynamics. A multi-user secure communications scheme is also considered where each user's symbol signal is mapped through a unique scramble matrix. We also examine whether these scramble matrices help to combat against smart jamming. The proposed system does not require self-synchronization between the chaotic receiver and transmitter and exhibits reasonable performance at relatively low SNRs.

Short-term predictions by statistical methods in regions of varying dynamical error growth in a chaotic system

2015 ◽  
Vol 127 (4) ◽  
pp. 457-465 ◽  
Author(s):  
A. K. Mittal ◽  
U. P. Singh ◽  
A. Tiwari ◽  
S. Dwivedi ◽  
M. K. Joshi ◽  
...  
Keyword(s):  
Statistical Methods ◽  
Chaotic System ◽  
Error Growth ◽  
Short Term ◽  
Dynamical Error
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