Open AccessBook Chapter
A Tutorial on Particle Filtering and Smoothing: Fifteen years later
Arnaud Doucet,Adam M. Johansen +1 more
TLDR
A complete, up-to-date survey of particle filtering methods as of 2008, including basic and advanced particle methods for filtering as well as smoothing.Abstract:
Optimal estimation problems for non-linear non-Gaussian state-space models do not typically admit analytic solutions. Since their introduction in 1993, particle filtering methods have become a very popular class of algorithms to solve these estimation problems numerically in an online manner, i.e. recursively as observations become available, and are now routinely used in fields as diverse as computer vision, econometrics, robotics and navigation. The objective of this tutorial is to provide a complete, up-to-date survey of this field as of 2008. Basic and advanced particle methods for filtering as well as smoothing are presented.read more
Citations
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Particle Markov chain Monte Carlo methods
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Bayesian Reasoning and Machine Learning
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Digital Twin: Values, Challenges and Enablers From a Modeling Perspective
TL;DR: This work reviews the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.
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A Review of Computer Vision Techniques for the Analysis of Urban Traffic
TL;DR: A comprehensive review of the state-of-the-art computer vision for traffic video with a critical analysis and an outlook to future research directions is presented.
Proceedings ArticleDOI
Unfreezing the robot: Navigation in dense, interacting crowds
Pete Trautman,Andreas Krause +1 more
TL;DR: IGP is developed, a nonparametric statistical model based on dependent output Gaussian processes that can estimate crowd interaction from data that naturally captures the non-Markov nature of agent trajectories, as well as their goal-driven navigation.
References
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.
Book
Monte Carlo Statistical Methods
TL;DR: This new edition contains five completely new chapters covering new developments and has sold 4300 copies worldwide of the first edition (1999).
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.
Journal Article
Optimal Filtering
TL;DR: This book helps to fill the void in the market and does that in a superb manner by covering the standard topics such as Kalman filtering, innovations processes, smoothing, and adaptive and nonlinear estimation.