Length: 3 hours
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
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