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
Geoacoustic and source tracking using particle filtering: experimental results.
TL;DR: A particle filtering approach is presented for performing sequential geoacoustic inversion of a complex ocean acoustic environment using a moving acoustic source and is an ideal algorithm to perform tracking of environmental and source parameters, and their uncertainties via the evolving posterior probability densities.
Patent
Tracking system and method employing multiple overlapping sensors
TL;DR: In this paper, a tracking system and method of estimating position and velocity of an object are provided, which includes first and second sensors for sensing an object in first-and second-view fields of view, respectively.
Posted Content
Nonlinear Bayesian Estimation: From Kalman Filtering to a Broader Horizon
TL;DR: In this paper, the authors present an up-to-date tutorial review of nonlinear Bayesian estimation methods, including Gaussian filtering, Gaussian-sum filtering, particle filtering and moving horizon estimation.
Journal ArticleDOI
Uniform time average consistency of Monte Carlo particle filters
TL;DR: In this article, it was shown that bootstrap-type Monte Carlo particle filters approximate the optimal nonlinear filter in a time average sense uniformly with respect to the time horizon when the signal is ergodic and the particle system satisfies a tightness property.
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.