Format The course runs over two consecutive days with MATLAB demos provided by the lecturers. Course Outline You will learn the fundamentals of target tracking and sensor fusion. The focus will be on the Bayesian estimation framework. After a short overview of the course, you will learn about: – Tracking filters, such as the optimal Bayes filter, various Kalman-type filters, particle filters and the IMM filter for manoeuvring targets. – Random-finite-set (RFS) based tracking filters, such as the Bernoulli filter and the PHD filter. – Multi-target tracking methods, with the focus on various data association techniques. – Multi-sensor tracking, with the emphasis on distributed and decentralised fusion. – Sensor control for “active” tracking (partially observed Markov decision process). – Advanced methods, such as Track-before-detect, possibilistic tracking, detecting anomalies. – Selected applications, such as computer vision, robotics, and search. For more information please visit: https://www.eventbrite.com.au/e/introduction-to-tracking-and-sensor-fusion-tickets-639909415287?aff=ebdsoporgprofile Speaker(s): Branko Ristic, Du Yong Kim Bldg: Innovation House – Offices and Conference Centre , 50 Mawson Lakes Boulevard, Mawson Lakes, South Australia, Australia, 5095