Multi-object System: A Stochastic Geometric Approach
Abstract:
Multi-object systems are complex dynamical systems in which the number of objects and their states are unknown and vary randomly with time. Multi-object systems arise in many application areas, including defence, computer vision, robotics, biomedical research and machine learning. Indeed most systems in nature are multi-object systems. The last decade has witnessed exciting developments in state estimation and control for multi-object system with the introduction of stochastic geometry to the field. The history of stochastic geometry traces back to the problem of Buffon’s needle and has long been used by statisticians in many diverse applications including astronomy, atomic physics, biology, sampling theory, stereology, etc. Since the early 2000s, Mahler’s seminal work on stochastic geometric models for multi-object filtering has continued to attract substantial interests from academia and industry alike. This seminar presents an overview of the stochastic geometric approach to multi-object systems and outlines recent developments in areas such as sensor control, computer vision, and field robotics.
Short Bio:
Ba-Ngu Vo received his Bachelor degrees jointly in Science and Electrical Engineering with first class honours in 1994, and PhD in 1997. He had held various research positions at various institutions before joining the University of Western Australia as Winthrop Professor in 2010. Currently he is Professor and Chair of Signals and Systems in the Department of Electrical and Computer Engineering at Curtin University. Vo is a recipient of the Australian Research Council’s inaugural Future Fellowship and the 2010 Eureka Prize for Outstanding Science in support of Defense or National Security. He is an associate editor of the IEEE Transaction on Aerospace and Electronic System. He is best known as a pioneer in the stochastic geometric approach to multi-object system. His research interests are signal processing, systems theory and stochastic geometry with emphasis on target tracking, space situational awareness, robotics and computer vision.
