S
Simon Maskell
Researcher at University of Liverpool
Publications - 155
Citations - 15562
Simon Maskell is an academic researcher from University of Liverpool. The author has contributed to research in topics: Particle filter & Computer science. The author has an hindex of 27, co-authored 128 publications receiving 14367 citations. Previous affiliations of Simon Maskell include Qinetiq & University of Cambridge.
Papers
More filters
Journal ArticleDOI
Group Object Structure and State Estimation With Evolving Networks and Monte Carlo Methods
TL;DR: The effectiveness of the proposed approach is illustrated over scenarios for group motion estimation in urban environments, and results with challenging scenarios with merging, splitting, and crossing of groups are presented with high estimation accuracy.
Proceedings ArticleDOI
Particle-based track-before-detect in Rayleigh noise
TL;DR: In this paper, an efficient particle filter TBD algorithm is presented, which models the signal processing stages which may be found in a sensor such as radar, and it is shown that in a simple simulation the algorithm can detect and track targets with a signalto-noise ratio as low as 3dB.
Journal ArticleDOI
A Bayesian approach to fusing uncertain, imprecise and conflicting information
TL;DR: This paper defines Bayesian models that articulate uncertainty over the value of probabilities (including multimodal distributions that result from conflicting information) and uses a minimum expected cost criterion to facilitate making decisions that involve hypotheses that are not mutually exclusive.
Journal ArticleDOI
Recommendations for the Use of Social Media in Pharmacovigilance: Lessons from IMI WEB-RADR
John van Stekelenborg,Johan Ellenius,Simon Maskell,Tomas Bergvall,Ola Caster,Nabarun Dasgupta,Juergen Dietrich,Sara Gama,David J. Lewis,David J. Lewis,Victoria Newbould,Sabine Brosch,Carrie E. Pierce,Gregory Powell,Alicia Ptaszynska-Neophytou,Antoni F. Z. Wiśniewski,Phil Tregunno,G. Niklas Norén,Munir Pirmohamed,Munir Pirmohamed +19 more
TL;DR: From this original research, several recommendations are presented with supporting rationale and consideration of the limitations, and novel text and data mining methods for social media analysis have been developed and evaluated.
Proceedings ArticleDOI
A rao-blackwellised unscented Kalman filter
TL;DR: In this article, Rao-Blackwellisation is used to calculate tractable integrations in the unscented Kalman filter, which leads to a re-duction in the quasi-Monte Carlo variance, and a decrease in the computational complexity by considering a common tracking problem.