Peter Willett has been a faculty member in the Electrical and Computer Engineering Department at the University of Connecticut since 1986. Since 1998 he has been a Professor, and since 2003 an IEEE Fellow. His primary areas of research have been statistical signal processing, detection, machine learning, communications, data fusion, and tracking. He is chief editor of IEEE AESS Magazine (2018-2020). He was editor-in-chief of IEEE Signal Processing Letters, 2014-2016 and before that for IEEE Transactions on Aerospace and Electronic Systems from 2006-2011. He was also AESS Vice President for Publications 2012-2014. He is a member of the IEEE Fellows Committee and Periodicals Committee, and was a member of the Signal Processing Society’s Technical Activities and Conference Executive Boards. He is a member of the IEEE AESS Board of Governors and was the Chair of IEEE Signal Processing Society’s Sensor-Array and Multichannel (SAM) technical committee.

Data Association and Target Tracking

To thread measurements (well, many call them “hits” or “plots”) of radar, sonar or imaging observations to a credible, smooth and reportable trajectory requires a filter. We’ll discuss those – Kalman, Unscented, particle, etc. – very briefly. But one cannot even begin to filter without knowing which hits come from which targets, and which hits are complete nonsense (clutter). When wrapped inside some scheme for such data-association, a filter becomes a tracker. This talk explains, at a fairly high level, the intuition behind some of the popular tracking algorithms.