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Random Set Filtering Techniques for

Multi-Object State Estimation and Multi-Target Tracking

The random-set filtering approach to multiple-target tracking using Finite Set Statistics (FISST) proposed by Ron Mahler is receiving increasing interest due to the success of practical implementations of Probability Hypothesis Density (PHD/CPHD) filters. The aim of this website is to provide a focal point for research activities related to this work including a list of articles over the last 15 years with links to some key results. We also provide a list of researchers working in this field internationally with over a dozen countries represented.

In order to facilitate greater awareness and appreciation of this work, we provide a background introduction to this field. In the demonstration section we show tracking videos on a range of different sensor data including sonar, radar, video and millimetre wave images, in addition to simulated examples illustrating the performance in more controlled scenarios. We encourage researchers working in this area to contribute papers, theses and tracking videos, and let us know of relevant events and vacancies that we can publicise.

Contact: Daniel Clark or Ba-Ngu Vo

Heriot-Watt University and the University of Melbourne 2008