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 generic approach to simultaneous tracking and verification in video
Baoxin Li,Rama Chellappa +1 more
TL;DR: A generic approach to simultaneous tracking and verification in video data is presented, based on posterior density estimation using sequential Monte Carlo methods, and several applications are illustrated by experiments devised to evaluate its performance.
Proceedings ArticleDOI
Clustering using sum-of-norms regularization: With application to particle filter output computation
TL;DR: A novel clustering method that uses a sum-of-norms (SON) regularization to control the tradeoff between the model fit and the number of clusters is presented, formulated as a convex optimization problem.
Book
Particle Filters for Random Set Models
TL;DR: This bookdiscusses state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or Stochastic filtering and is based on the Monte Carlo statistical method.
DissertationDOI
Discriminative vision-based recovery and recognition of human motion
TL;DR: An example-based pose recovery approach is introduced where histograms of oriented gradients (HOG) are used as the image descriptor and simple functions are used to discriminate between two classes after applying a common spatial patterns (CSP) transform on sequences of HOG descriptors.
Journal ArticleDOI
Estimation of parameters in a linear state space model using a Rao-Blackwellised particle filter
TL;DR: In this paper, a Rao-Blackwellised particle filter is used in the estimation of the parameters of a linear stochastic state space model for condition monitoring in a railway vehicle dynamic model.
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.