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
Inference for nonlinear epidemiological models using genealogies and time series.
TL;DR: The approach using a nonlinear Susceptible-Infected-Recovered (SIR) model for the transmission dynamics of an infectious disease is demonstrated and shown that it provides accurate estimates of past disease dynamics and key epidemiological parameters from genealogies with or without accompanying time series data.
Journal ArticleDOI
Particle Filtering for Multisensor Data Fusion With Switching Observation Models: Application to Land Vehicle Positioning
TL;DR: A family of efficient particle filters is proposed, for both synchronous and asynchronous sensor observations as well as for important special cases, and a wheel land vehicle positioning problem where the GPS information may be unreliable because of multipath/masking effects is studied.
Journal ArticleDOI
Recursive Monte Carlo filters: algorithms and theoretical analysis
TL;DR: In this paper, the accept-reject version of the recursive Monte Carlo filter is compared with the more common sampling importance resampling version of particle filters. And the central limit theorem for particle filters is proved.
Journal ArticleDOI
Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks
TL;DR: In this article, a method for predicting the end-of-discharge of Li-Ion rechargeable batteries is proposed, which combines particle filters with radial basis function neural networks.
Book
Dynamical bias in the coin toss
TL;DR: It is shown that vigorously flipped coins tend to come up the same way they started, and the limiting chance of coming up this way depends on a single parameter, the angle between the normal to the coin and the angular momentum vector.
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.