Open AccessProceedings Article
The Unscented Particle Filter
Rudolph van der Merwe,Arnaud Doucet,Nando de Freitas,Eric A. Wan +3 more
- Vol. 13, pp 584-590
Reads0
Chats0
TLDR
This paper proposes a new particle filter based on sequential importance sampling that outperforms standard particle filtering and other nonlinear filtering methods very substantially and is in agreement with the theoretical convergence proof for the algorithm.Abstract:
In this paper, we propose a new particle filter based on sequential importance sampling. The algorithm uses a bank of unscented filters to obtain the importance proposal distribution. This proposal has two very "nice" properties. Firstly, it makes efficient use of the latest available information and, secondly, it can have heavy tails. As a result, we find that the algorithm outperforms standard particle filtering and other nonlinear filtering methods very substantially. This experimental finding is in agreement with the theoretical convergence proof for the algorithm. The algorithm also includes resampling and (possibly) Markov chain Monte Carlo (MCMC) steps.read more
Citations
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.
Book
Machine Learning : A Probabilistic Perspective
TL;DR: This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach, and is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
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 ArticleDOI
Kernel-based object tracking
TL;DR: A new approach toward target representation and localization, the central component in visual tracking of nonrigid objects, is proposed, which employs a metric derived from the Bhattacharyya coefficient as similarity measure, and uses the mean shift procedure to perform the optimization.
Proceedings ArticleDOI
The unscented Kalman filter for nonlinear estimation
Eric A. Wan,R. van der Merwe +1 more
TL;DR: The unscented Kalman filter (UKF) as discussed by the authors was proposed by Julier and Uhlman (1997) for nonlinear control problems, including nonlinear system identification, training of neural networks, and dual estimation.
References
More filters
Book
Bayesian Data Analysis
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
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.
Proceedings ArticleDOI
New extension of the Kalman filter to nonlinear systems
Simon Julier,Jeffrey Uhlmann +1 more
TL;DR: It is argued that the ease of implementation and more accurate estimation features of the new filter recommend its use over the EKF in virtually all applications.
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.