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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Proceedings ArticleDOI
Probabilistic tracking in a metric space
Kentaro Toyama,Andrew Blake +1 more
TL;DR: A new exemplar-based, probabilistic paradigm for visual tracking is presented, which provides alternatives to standard learning algorithms by allowing the use of metrics that are not embedded in a vector space and eliminates any need for an assumption of probabilistically pixelwise independence.
Journal ArticleDOI
Probabilistic recognition of human faces from video
TL;DR: Recognition of human faces using a gallery of still or video images and a probe set of videos is systematically investigated using a probabilistic framework and a computationally efficient sequential importance sampling (SIS) algorithm is developed to estimate the posterior distribution.
Journal ArticleDOI
Intelligent Particle Filter and Its Application to Fault Detection of Nonlinear System
Shen Yin,Xiangping Zhu +1 more
TL;DR: In this paper, a modified particle filter, i.e., intelligent particle filter (IPF), is proposed, inspired from the genetic algorithm, which mitigates particle impoverishment and provides more accurate state estimation results compared with the general PF.
Journal ArticleDOI
A dual Kalman filter approach for state estimation via output-only acceleration measurements
TL;DR: In this article, a dual implementation of the Kalman filter for estimating the unknown input and states of a linear state-space model by using sparse noisy acceleration measurements is proposed, which avoids numerical issues attributed to unobservability and rank deficiency of the augmented formulation of the problem.
Journal ArticleDOI
Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and Bayesian model averaging
TL;DR: The present study compares the performance and applicability of the EnKF and BMA for probabilistic ensemble streamflow forecasting, an application for which a robust comparison of the predictive skills of these approaches can be conducted and suggests that for the watershed under consideration, BMA cannot achieve a performance matching that of theEnKF method.
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.