Message Passing and Hierarchical Models for Simultaneous Tracking and Registration

David Cormack, James Hopgood

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Sensor registration is an important problem that must be considered when attempting to perform any kind of data fusion in multi-modal, multi-sensor target tracking. In this Multiple Target Tracking (MTT) application, any inaccuracies in the registration can lead to false tracks being created, and tracks of true targets being stopped prematurely. This article introduces a method for simultaneously tracking multiple targets in a surveillance region and estimating appropriate sensor registration parameters so that sensor fusion can be performed accurately. The proposed method is based around particle Belief Propagation (BP), a recent but highly efficient framework for tracking multiple targets. The proposed method also uses a hierarchical model which allows for multiple processes to be linked and interact with one another. We present a comprehensive set of simulations and results using differing sensor setups, and compare with a Random Finite Set (RFS) approach, namely the Sequential Monte Carlo (SMC)-Probability Hypothesis Density (PHD) filter. The results show the proposed method is 17% more accurate than the RFS approach on average.
Original languageEnglish
JournalIEEE Transactions on Aerospace and Electronic Systems
Early online date5 Jan 2021
DOIs
Publication statusE-pub ahead of print - 5 Jan 2021

Keywords / Materials (for Non-textual outputs)

  • Radar
  • Sensor fusion
  • sensor registration
  • camera
  • multiple target tracking
  • PHD Filter
  • Belief propagation
  • Message passing

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