Intended Audience: Anyone who is interested in multi-target tracking.

Description: The Finite Set Statistics framework for multi-sensor multi-target tracking has attached considerable interest in recent years. It provides a unified perspective of multi-target tracking in a very intuitive manner by drawing direct parallels with the simpler problem of single-target tracking. This framework has lead to the development of multi-target filters such as the Probability Hypothesis Density (PHD), Cardinalized PHD (CPHD), Multi-Bernoulli filters and recently, the Generalized Labeled Multi-Bernoulli filter. In this tutorial, we show how these filters are implemented and illustrate via Matlab how these filters work. In particular, the tutorial will present the implementations of

• (1) Single target tracking in clutter
  
• (2) Bernoulli filter
  
• (3) Multi-Bernoulli filter
  
• (4) PHD and CPHD filters
  
• (5) Generalized Labeled Multi-Bernoulli filter.
  

Matlab code will be provided to the participants. It is envisaged that participants will come away with sufficient know-how to implement and apply these algorithms in their work.

Prerequisites: Working knowledge of random variable, probability density function, Gaussian distribution, and concepts such as state space models. Taking Ron Mahler's companion tutorial " A Finite-Set Statistics Prime" is desirable. This tutorial is also a prerequisite for Stephan Reuter and Karl Granstrom's tutorial on extended target tracking.

Presenter: Ba-Ngu Vo and Ba-Tuong Vo

Ba-Ngu Vo received his B.Sc. degree in Pure Mathematics and B.E. degree in Electrical Engineering with first class honors in 1994, and PhD in 1997. He had held various research positions before joining the department of Electrical and Electronic Engineering at the University of Melbourne in 2000. In 2010, he joined the School of Electrical Electronic and Computer Engineering at the University of Western Australia as Winthrop Professor and Chair of Signal Processing. Currently he is Professor and Chair of Signals and Systems in the Department of Electrical and Computer Engineering at Curtin University. Prof. Vo is a recipient of the Australian Research Council’s inaugural Future Fellowship His research interests are Signal Processing, Systems Theory and Stochastic Geometry with emphasis on target tracking, robotics, computer vision and space situational awareness.

Ba-Tuong Vo received the B.Sc. degree in applied mathematics and B.E. degree in electrical and electronic engineering (with first-class honors) in 2004 and the Ph.D. degree in engineering (with Distinction) in 2008, all from the University of Western Australia. He is currently an Associate Professor in the Department of Electrical and Computer Engineering at Curtin University and a recipient of an Australian Research Council Fellowship. His primary research interests are in point process theory, filtering and estimation, and multiple object filtering.

Both presenters were recipients of the 2010 Australian Museum DSTO Eureka Prize for "Outstanding Science in Support of Defence or National Security".