Journal ArticleDOI
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
Neil Gordon,David Salmond,Adrian F. M. Smith +2 more
- Vol. 140, Iss: 2, pp 107-113
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TLDR
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.Abstract:
An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linear- ity or Gaussian noise: it may be applied to any state transition or measurement model. A simula- tion example of the bearings only tracking problem is presented. This simulation includes schemes for improving the efficiency of the basic algorithm. For this example, the performance of the bootstrap filter is greatly superior to the standard extended Kalman filter.read more
Citations
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Journal ArticleDOI
A Bayesian approach to tracking multiple targets using sensor arrays and particle filters
TL;DR: A Bayesian approach to tracking the direction-of-arrival (DOA) of multiple moving targets using a passive sensor array using a collection of target states that can be viewed as samples from the posterior of interest.
Journal ArticleDOI
Importance sampling: a review
Surya T. Tokdar,Robert E. Kass +1 more
TL;DR: An overview of importance sampling—a popular sampling tool used for Monte Carlo computing and its mathematical foundation and properties that determine its accuracy in Monte Carlo approximations are discussed.
Journal ArticleDOI
On-Line Inference for Hidden Markov Models via Particle Filters.
Paul Fearnhead,Peter Clifford +1 more
TL;DR: This work considers the on‐line Bayesian analysis of data by using a hidden Markov model, where inference is tractable conditional on the history of the state of the hidden component, and shows that a new particle filter algorithm is introduced and shown to produce promising results when analysing data of this type.
Journal ArticleDOI
Adaptive Multiple Importance Sampling
TL;DR: Although the convergence properties of the algorithm cannot be investigated, it is demonstrated through a challenging banana shape target distribution and a population genetics example that the improvement brought by this technique is substantial.
Journal ArticleDOI
Message Passing Algorithms for Scalable Multitarget Tracking
Florian Meyer,Thomas Kropfreiter,Jason L. Williams,Roslyn A. Lau,Franz Hlawatsch,Paolo Braca,Moe Z. Win +6 more
TL;DR: This tutorial paper advocates a recently proposed paradigm for scalable multitarget tracking that is based on message passing or, more concretely, the loopy sum–product algorithm, which provides a highly effective, efficient, and scalable solution to the probabilistic data association problem, a major challenge in multitargettracking.
References
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BookDOI
Density estimation for statistics and data analysis
TL;DR: The Kernel Method for Multivariate Data: Three Important Methods and Density Estimation in Action.
Book
Stochastic Processes and Filtering Theory
TL;DR: In this paper, a unified treatment of linear and nonlinear filtering theory for engineers is presented, with sufficient emphasis on applications to enable the reader to use the theory for engineering problems.
Journal ArticleDOI
Nonlinear Bayesian estimation using Gaussian sum approximations
D. Alspach,H. Sorenson +1 more
TL;DR: In this paper an approximation that permits the explicit calculation of the a posteriori density from the Bayesian recursion relations is discussed and applied to the solution of the nonlinear filtering problem.
Journal Article
Bayesian statistics without tears: A sampling-resampling perspective
TL;DR: In this article, a sampling-resampling perspective on Bayesian inference is presented, which has both pedagogic appeal and suggests easily implemented calculation strategies, such as sampling-based methods.