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Modeling vessel kinematics using a stochastic mean-reverting process for long-term prediction
- Source :
- IEEE Transactions on Aerospace and Electronic Systems. 52:2313-2330
- Publication Year :
- 2016
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2016.
-
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 model, an analysis of a significant portion of real-world maritime traffic is provided.
- Subjects :
- 020301 aerospace & aeronautics
Mathematical optimization
Stochastic process
Process (computing)
Aerospace Engineering
020206 networking & telecommunications
Time scaling
02 engineering and technology
Kinematics
Term (time)
0203 mechanical engineering
Orders of magnitude (time)
0202 electrical engineering, electronic engineering, information engineering
Mean reversion
Applied mathematics
Electrical and Electronic Engineering
Long-term prediction
Mathematics
Subjects
Details
- ISSN :
- 00189251
- Volume :
- 52
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Aerospace and Electronic Systems
- Accession number :
- edsair.doi...........681a8fb0b2385c5ef35072bff8a6d13e