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
Progressive correction for regularized particle filters
Nadia Oudjane,Christian Musso +1 more
TL;DR: A new version of regularized particle filter is presented using a progressive correction (PC) principle which improves the approximation, in introducing a decreasing sequence of (fictitious) matrices for the observation noise.
Proceedings ArticleDOI
Variational inference for visual tracking
TL;DR: This paper introduces a variational approximation to the tracking recursion, and is shown to compare favorably with particle filtering techniques on a synthetic example and two real tracking problems.
The Dawning of the Age of Stochasticity
TL;DR: For over two millennia, Aristotle's logic has ruled over the thinking of western intellectuals as discussed by the authors, but from its shady beginnings devising gambling strategies and counting corpses in medieval London, probability theory and statistical inference now emerge as better foun-dations for scientific models, especially those of the process of thinking and as essential ingredients of theoretical mathematics.
Journal ArticleDOI
Vision and RFID data fusion for tracking people in crowds by a mobile robot
TL;DR: An active perception system, consisting of a camera mounted on a pan-tilt unit and a 360^o RFID detection system, both embedded on a mobile robot, and a multi-sensor-based control strategy based on the tracker outputs and on the RFID data is designed.
Journal ArticleDOI
Various Ways to Compute the Continuous-Discrete Extended Kalman Filter
TL;DR: An overview of the numerical methods, including recent works, usually implemented to approximate this filter are presented and Comparisons of theses methods on two different nonlinear models are finally presented.
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.