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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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Book ChapterDOI
Particle Filters for Visual Tracking
TL;DR: The application of particle filter in visual tracking, including single object and multiple objects tracking, and the contributions related to dealing with occlusion, interaction, illumination change using improved particle filters are discussed.
Book ChapterDOI
Mobile Robot Localization Method Based on Adaptive Particle Filter
Yimin Xia,Yimin Yang +1 more
TL;DR: This paper puts forward the mobile robot localization method based on adaptive particle filter (ADF), and shows that ADF improved the systematic computational efficiency and precision of localization inMobile robot localization.
Proceedings ArticleDOI
Target tracking in an urban warfare environment using particle filters
TL;DR: An adaptive particle filter framework in which the numbers of particles, fusion parameters, as well as filter parameters are updated during the filtering process, and a neural network is used to determine how well the filter is performing.
Proceedings ArticleDOI
Identification of nonlinear state space models using an MLP network trained by the EM algorithm
TL;DR: A new approach for modeling a discrete time nonlinear state space system with a multi layer perceptron (MLP) neural network with expectation maximization algorithm is proposed.
Proceedings ArticleDOI
Improved adaptive particle filter using adjusted variance and gradient data
TL;DR: A new APF is proposed, which adjusts the variance and then, uses the gradient data to generate samples near the high likelihood region, and performs better than the standard particle filter and the APF using Kullback-Leibler Distance (KLD) sampling.