Back to Search
Start Over
Bayesian Track-to-Graph Association for Maritime Traffic Monitoring
- Source :
- EUSIPCO
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
Abstract
- We present a hypothesis test to associate ship track measurements to an edge of a given graph that statistically models common traffic routes in a given area of interest. The association algorithm is based on the hypothesis that ship velocities are modeled by mean-reverting stochastic processes. Prior knowledge about the traffic is provided by the graph in form of probability density functions of the mean-reverting kinematic parameters for each node and edge of the graph, that are exploited in the formalization of the association algorithm. Tests on real Automatic Identification System (AIS) data show a qualitatively good association performance. Future developments of this work include the development of specific quantitative metrics to assess the association performance.
- Subjects :
- 020301 aerospace & aeronautics
Signal processing
Automatic Identification System
Computer science
Stochastic process
Bayesian probability
020206 networking & telecommunications
Probability density function
02 engineering and technology
Kinematics
computer.software_genre
Graph
law.invention
0203 mechanical engineering
law
0202 electrical engineering, electronic engineering, information engineering
Graph (abstract data type)
Data mining
computer
Statistical hypothesis testing
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2018 26th European Signal Processing Conference (EUSIPCO)
- Accession number :
- edsair.doi...........f1d91e72df49206d652dc024d9e874f6