Message Passing for Joint Registration and Tracking in Multistatic Radar

David Cormack, James Hopgood

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

Abstract / Description of output

Sensor registration is fundamental in sensor fusion. Inaccuracies in sensor location and rotation can manifest themselves into the measurements used in Multi-Target Tracking (MTT), and dramatically degrade its performance. These registration parameters are often estimated separately to any multi-target estimation, which could lead to increased computational expense, and also to systematic errors. Recent works have shown that MTT algorithms derived from Belief-Propagation are computationally efficient and highly scalable for large tracking scenarios. This work presents a hierarchical Bayesian model inspired by single-cluster methods from the Random Finite Set (RFS) literature, that allow for the registration parameters to be estimated jointly with the multiple target tracking. Simulations are carried out on a multistatic radar network containing two radars with a relative range and azimuth bias between them. Results are presented for a particle-BP MTT algorithm, and it's performance is compared to that of a Sequential Monte Carlo-Probability Hypothesis Density (PHD) filter. The results show that the BP algorithm outperforms the PHD implementation in terms of accuracy by around 10.
Original languageEnglish
Title of host publication2019 Sensor Signal Processing for Defence Conference (SSPD)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)978-1-7281-0503-1
Publication statusE-pub ahead of print - 1 Jul 2019
EventSensor Signal Processing for Defence (SSPD) 2019 - Hilton Brighton Metropole Hotel, Brighton, United Kingdom
Duration: 9 May 201910 May 2019


ConferenceSensor Signal Processing for Defence (SSPD) 2019
Abbreviated titleSSPD 2019
Country/TerritoryUnited Kingdom
Internet address


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