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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Book ChapterDOI
Particle Learning for Sequential Bayesian Computation
TL;DR: This work uses an essential state vector together with a predictive and propagation rule to build a resampling-sampling framework for the construction of sequential posterior sampling strategies for a variety of commonly used models.
Journal ArticleDOI
Resampling methods for particle filtering: identical distribution, a new method, and comparable study
Tian-cheng Li,Tian-cheng Li,Gabriel Villarrubia,Shudong Sun,Juan M. Corchado,Juan M. Corchado,Javier Bajo +6 more
TL;DR: More than a dozen typical resampling methods are compared via simulations in terms of sample size variation, sampling variance, computing speed, and estimation accuracy, providing solid guidelines for either selection of existing resamplings methods or new implementations.
Journal ArticleDOI
Improved particle filters for multi-target tracking
Vasileios Maroulas,Panos Stinis +1 more
TL;DR: A novel approach based on drift homotopy for stochastic differential equations is presented for improving particle filters for multi-target tracking with a nonlinear observation model and the numerical results show that the suggested approach can improve significantly the performance of a particle filter.
Proceedings ArticleDOI
The marginalized particle filter in practice
TL;DR: The marginalized particle filter as discussed by the authors is a powerful combination of the particle filter and the Kalman filter, which can be used when the underlying model contains a linear sub-structure, subject to Gaussian noise.
Proceedings ArticleDOI
Gaussian Particle Implementations of Probability Hypothesis Density Filters
TL;DR: In this article, a new particle implementation of the probability hypothesis density (PHD) filter is presented, which does not require clustering to determine target states and is restricted to linear Gaussian target dynamics, since it uses the Kalman filter to estimate the means and covariances of the Gaussians.
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.