Prof. Peter Willett
Prof. Peter Willett, University of Connecticut, USA /Vice President, IEEE AES Society
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Biography:
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 was editor-in-chief for IEEE Transactions on Aerospace and Electronic Systems from 2006-2011, and is now the VP for Publications for the IEEE AES Society. For 1998-2005 he was associate editor for three active journals – IEEE Transactions on Aerospace and Electronic Systems (for Data Fusion and Target Tracking) and IEEE Transactions on Systems, Man, and Cybernetics, parts A and B. He is remains associate editor for the IEEE AES Magazine, and ISIF’s Journal of Advances in Information Fusion, and is a member of the editorial board of IEEE’s Special Topics in Signal Processing journal and a senior editor for IEEE Signal Processing Letters. He has been a member of the IEEE AESS Board of Governors (2003-2009, 2011-) and of ISIF 2010-2013. He was General Co-Chair (with Stefano Coraluppi) of IEEE/ISIF International Conference for Information Fusion (ICIF) in Florence in 2006, again was Executive Co-Chair (with Wolfgang Koch) for ICIF in Cologne in 2008, and was also Emeritus Chair (with Darin Dunham and Amy Smith-Carroll) for ICIF in Chicago in 2011. He was Program Co-Chair (with Eugene Santos) of the IEEE Conference on Systems, Man, and Cybernetics, held in Washington, DC in October of 2003. He was Program Co-Chair (with PramodVarshney) for the 1999 ICIF. He has been a member of the IEEE Signal Processing Society’s Sensor-Array and Multichannel (SAM) technical committee since 1997 and is now Vice Chair.
Title: A Primer on Data Association
Abstract:
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 the main topic here arises because 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 is intended to explain, at a fairly high level, the intuition behind some of the popular tracking algorithms.
