Title
Modeling vessel kinematics using a stochastic mean-reverting process for long-term prediction.
Abstract
We present a novel method for predicting long-term target states based on mean-reverting stochastic processes. We use the Ornstein-Uhlenbeck (OU) process, leading to a revised target state equation and to a time scaling law for the related uncertainty that in the long term is shown to be orders of magnitude lower than under the nearly constant velocity (NCV) assumption. In support of the proposed ...
Year
DOI
Venue
2016
10.1109/TAES.2016.150596
IEEE Transactions on Aerospace and Electronic Systems
Keywords
Field
DocType
Mathematical model,Predictive models,Trajectory,Uncertainty,Stochastic processes,Target tracking
Applied mathematics,Equation of state,Orders of magnitude (numbers),Mathematical optimization,Kinematics,Long-term prediction,Control theory,Stochastic process,Time scaling,Mean reversion,Mathematics,Trajectory
Journal
Volume
Issue
ISSN
52
5
0018-9251
Citations 
PageRank 
References 
9
0.82
0
Authors
4
Name
Order
Citations
PageRank
Leonardo M. Millefiori1398.46
Paolo Braca246746.44
Karna Bryan31076.94
Peter Willett41962224.14