Simon Godsill

Simon Godsill is Professor of Statistical Signal Processing in the Engineering Department at Cambridge University. He is also a Professorial Fellow and tutor at Corpus Christi College Cambridge. He coordinates an active research group in Signal Inference and its Applications within the Signal Processing and Communications Laboratory at Cambridge, specializing in Bayesian computational methodology, multiple object tracking, audio and music processing, and financial time series modeling. A particular methodological theme over recent years has been the development of novel techniques for optimal Bayesian filtering and smoothing, using Sequential Monte Carlo or Particle Filtering methods. Prof. Godsill has published extensively in journals, books and international conference proceedings, and has given a number of high profile invited and plenary addresses at conferences such as the Valencia conference on Bayesian Statistics, the IEEE Statistical Signal Processing Workshop and the Conference on Bayesian Inference for Stochasrtic Processes (BISP). He co-authored a seminal Springer text Digital Audio Restoration with Prof. Peter Rayner in 1998. He was technical chair of the successful IEEE NSSPW workshop in 2006 on sequential and nonlinear filtering methods, and has been on the conference panel for numerous other conferences/workshops. Prof. Godsill has served as Associate Editor for IEEE Tr.  ignal Processing and the journal Bayesian Analysis. He was Theme Leader in Tracking and Reasoning over Time for the UK’s Data and Information Fusion Defence Technology Centre (DIF-DTC) and Principal Investigator on many grants funded by the EU, EPSRC, QinetiQ, General Dynamics, MOD, Microsoft UK, Citibank and Mastercard. In 2009-10 he was co-organiser of an 18 month research program in Sequential Monte Carlo Methods at the SAMSI Institute in North Carolina. He is a  Director of CEDAR Audio Ltd. (which has received numerous accolades over the years, including a technical Oscar).

Points, particles and positions: recent advances in distributed processing of agile objects


In this talk I will discuss models developed under the SIGNeTS project for agile motion of objects. I will describe new motion and observation models based on point process theory and Levy processes, as well as new advances in Gaussian process models for nonparametric modelling of motion, and will further discuss methods for distributed processing of sensor data using these models, as well as inference about target detection rates and clutter rates. The methodology is probabilistic and implemented using combinations of particle filtering and variational methods.