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
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
Formulating, Identifying and Estimating the Technology of Cognitive and Noncognitive Skill Formation
Flavio Cunha,James J. Heckman +1 more
TL;DR: A dynamic factor model is estimated to solve the problem of endogeneity of inputs and multiplicity of inputs relative to instruments and the role of family environments in shaping these skills at different stages of the life cycle of the child.
Proceedings ArticleDOI
Monte Carlo localization for mobile robots
TL;DR: The Monte Carlo localization method is introduced, where the probability density is represented by maintaining a set of samples that are randomly drawn from it, and it is shown that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location.
BookDOI
Handbook of Face Recognition
Stan Z. Li,Anil K. Jain +1 more
TL;DR: This highly anticipated new edition provides a comprehensive account of face recognition research and technology, spanning the full range of topics needed for designing operational face recognition systems, as well as offering challenges and future directions.
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Inference in Hidden Markov Models
TL;DR: This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory, and builds on recent developments to present a self-contained view.
Proceedings ArticleDOI
A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking
Simon Maskell,Neil Gordon +1 more
TL;DR: Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.
References
More filters
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.