Bayesian Track-to-Graph Association for Maritime Traffic Monitoring

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, which 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.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2019
Accession Number
AD1113111

Entities

People

  • Leonardo M. Millefiori
  • Paolo Braca
  • Raffaele Grasso

Organizations

  • Centre for Maritime Research and Experimentation

Tags

Communities of Interest

  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Algorithms
  • Automatic Identification Systems
  • Command And Control
  • Command And Control Systems
  • Computational Science
  • Data Science
  • Data Sets
  • Differential Equations
  • Identification Systems
  • Information Science
  • Markov Processes
  • Measurement
  • Models
  • Monte Carlo Method
  • Probability
  • Stationary Processes
  • Stochastic Processes

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Graph Algorithms and Convex Optimization.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference