Sensor Registration and Tracking from Heterogeneous Sensors with Belief Propagation

David Cormack, James R. Hopgood

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


Modern sensor platforms carry an increasingly diverse range of sensors onboard, in order to estimate target positions inside their common surveillance region. Off-the-shelf sensors often provide measurements at different rates, and with different and potentially varying levels of uncertainty. Heterogeneous and asynchronous sensor networks make the sensor fusion problem more challenging as multiple measurement models and a dynamic prediction model are required. Moreover, a key challenge to address in fusion systems is that of sensor bias. Any relative bias between sensors could result in measurements not correlating with one another in a common frame of reference, and therefore vastly reducing track accuracy. This work presents novel results on a joint method that estimates both external angular bias between a radar and a camera, and the states of multiple targets. The proposed technique uses a particle-based implementation of Belief Propagation (BP), and compares with Random Finite Set (RFS)-based approaches. Initial results show that the BP approach outperforms the RFS approaches in terms of accuracy by around 50% when using the Optimal Sub-Pattern Assignment (OSPA) metric.

Original languageEnglish
Title of host publication22th International Conference on Information Fusion 2019
ISBN (Electronic)9780996452786
Publication statusPublished - 27 Feb 2020
Event22nd International Conference on Information Fusion 2019 - Ottawa, Canada
Duration: 2 Jul 20195 Jul 2019


Conference22nd International Conference on Information Fusion 2019
Abbreviated titleFUSION 2019


  • camera
  • data fusion
  • probabilistic graphical models
  • radar
  • sensor registration
  • target tracking

ASJC Scopus subject areas

  • Information Systems
  • Instrumentation


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