Intended Audience: This is a researchfocussed tutorial.

Description: There have been a number of important innovations in multitarget tracking and multisensor fusion in recent years that have had significant international impact across different application domains. In particular, the suite of mathematical tools used in Finite Set Statistics, such as point process models, have been developed specifically to enable such innovations.

Considering systems of multiple objects with point process models adopted from the applied probability literature enables advanced models to be constructed in a simple way. However, most mathematical work in spatial statistics and point process theory is presented in a measuretheoretic context which could potentially prevent engineering researchers interested in developing multiobject estimation algorithms for sensor fusion applications from exploring these rich domains.

This tutorial will highlight some basic mathematical concepts in multiobject estimation to enable researchers to better understand and contribute to innovations in this field. The goal of the presenters is to inspire participants to develop a broader mathematical perspective and explore the literature in spatial statistics and point processes to aid their research in sensor fusion. The presenters will highlight where new concepts to multiobject estimation in sensor fusion, such as regional variance for estimating population uncertainty, can be facilitated when considering a measuretheoretic point process perspective.

Prerequisites: Bayesian filtering. Knowledge of the PHD filter would be helpful.

Presenter: Daniel Clark, Emmanuel D. Delande, and Jérémie Houssineau

The instructors organised and ran the 2013 Summer School on Finite Set Statistics in Edinburgh (with Dstl UDRC sponsorship) and Albuquerque (with AFOSR sponsorship).

Daniel Clark is an Associate Professor in Sensors and Systems at HeriotWatt University. His research interests are in the development of the theory and applications of multiobject estimation algorithms for sensor fusion problems. He has collaborated closely with Dstl in the UK on a number of projects in multitarget tracking spanning theoretical algorithm development to practical deployment in collaboration with BAE Systems, Finnmechanica, Thales, and DCNS. He lectures mathematics to undergraduate electrical engineers and developed a course on “MultiSensor Fusion and Tracking” for a European Masters programme (Vibot). In 2014, he was a Visiting Professor at the University of Colorado where he gave a lecture course on multiobject estimation. He gave a tutorial in 2011 at ICASSP with Branko Ristic entitled “Particle filters for multiobject Bayes filtering and sensor control in the framework of random set theory”.

Emmanuel D. Delande received an Eng. degree from the Ecole Centrale de Lille, Lille, and a M.Sc. degree in automatic control and signal processing from the University of Science & Technology, Lille, both in 2008. He was awarded his Ph.D. in 2012 from the Ecole Centrale de Lille. He is a research associate at HeriotWatt University in Edinburgh. His research interests are in the design and the implementation of multiobject filtering solutions for multiple target tracking and sensor management problems.

Isabel Schlangen