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Editors: Sequential Monte Carlo Methods in Practice
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The article was published on 2001-01-01 and is currently open access. It has received 1215 citations till now. The article focuses on the topics: Dynamic Monte Carlo method & Monte Carlo method in statistical physics.read more
Citations
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Journal ArticleDOI
Kalman filtering with state constraints: a survey of linear and nonlinear algorithms
TL;DR: In this paper, the authors provide an overview of various ways to incorporate state constraints in the Kalman filter and its nonlinear modifications, including the unscented Kalman Filter, the particle filter, and the extended Kalman Filtering.
Posted Content
Comparison of Resampling Schemes for Particle Filtering
TL;DR: In this paper, a comparison of various resampling approaches that have been proposed in the literature on particle filtering is made, and it is shown using simple arguments that the so-called residual and stratified methods do yield an improvement over the basic multinomial re-sampling approach.
Journal ArticleDOI
Particle filters for state estimation of jump Markov linear systems
TL;DR: This paper presents efficient simulation-based algorithms called particle filters to solve the optimal filtering problem as well as the optimal fixed-lag smoothing problem forJump Markov linear systems.
Proceedings ArticleDOI
BraMBLe: a Bayesian multiple-blob tracker
Michael Isard,John MacCormick +1 more
TL;DR: A multi-blob likelihood function which assigns directly comparable likelihoods to hypotheses containing different numbers of objects and a Bayesian filter for tracking multiple objects when the number of objects present is unknown and varies over time are introduced.
Proceedings ArticleDOI
Robust visual tracking via multi-task sparse learning
TL;DR: Experimental results show that MTT methods consistently outperform state-of-the-art trackers and mining the interdependencies between particles improves tracking performance and overall computational complexity.