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Neil Gordon
Researcher at Aston University
Publications - 182
Citations - 38862
Neil Gordon is an academic researcher from Aston University. The author has contributed to research in topics: Particle filter & Negative luminescence. The author has an hindex of 37, co-authored 181 publications receiving 37011 citations. Previous affiliations of Neil Gordon include Qinetiq & University of Cambridge.
Papers
More filters
Journal ArticleDOI
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
TL;DR: Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.
Journal ArticleDOI
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
TL;DR: An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters, represented as a set of random samples, which are updated and propagated by the algorithm.
BookDOI
Sequential Monte Carlo methods in practice
TL;DR: This book presents the first comprehensive treatment of Monte Carlo techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection.
Book
Beyond the Kalman Filter: Particle Filters for Tracking Applications
TL;DR: Part I Theoretical concepts: introduction suboptimal nonlinear filters a tutorial on particle filters Cramer-Rao bounds for nonlinear filtering and tracking applications: tracking a ballistic object bearings-only tracking range- only tracking bistatic radar tracking targets through blind Doppler terrain aided tracking detection and tracking of stealthy targets group and extended object tracking.
Proceedings ArticleDOI
A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking
Simon Maskell,Neil Gordon +1 more
TL;DR: Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.