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
Wireless sensor networks localization algorithms: a comprehensive survey
TL;DR: This paper separates range-based schemes and range-free schemes into two types: fully schemes and hybrid schemes, and compares the most relevant localization algorithms and discusses the future research directions for wireless sensor networks localization schemes.
Journal ArticleDOI
Universal Residuals: A Multivariate Transformation.
TL;DR: This paper generalizes Rosenblatt's transformation so that it applies to arbitrary probability models, providing a tool for exploratory data analysis and formal goodness-of-fit testing for a very general class of probability models.
Journal ArticleDOI
Representing cyclic human motion using functional analysis
TL;DR: A robust automatic method for modeling cyclic 3D human motion such as walking using motion-capture data that can automatically deal with noise and missing data is presented.
Journal ArticleDOI
Particle Filters and Data Assimilation
Paul Fearnhead,Hans R. Künsch +1 more
TL;DR: The challenges posed by models with high-dimensional states, joint estimation of parameters and the state, and inference for the history of the state process are discussed, including methods based on the particle filter and the ensemble Kalman filter.
Journal ArticleDOI
Space Object Shape Characterization and Tracking Using Light Curve and Angles Data
TL;DR: In this article, the shape model of the resident space object constitutes the hypothesis and estimates of the likelihood of each hypothesis, given the available measurements, are provided from the multiple-model adaptive estimation approach.
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.