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
Reads0
Chats0
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
More filters
Journal ArticleDOI
Approximate evaluation of marginal association probabilities with belief propagation
Jason L. Williams,Roslyn A. Lau +1 more
TL;DR: A graphical model formulation of data association is presented and an approximate inference method, belief propagation (BP), is applied to obtain estimates of marginal association probabilities to prove that BP is guaranteed to converge, and bound the number of iterations necessary.
Journal ArticleDOI
An Adaptive Approach to Real-Time Aggregate Monitoring With Differential Privacy
Liyue Fan,Li Xiong +1 more
TL;DR: FAST, a novel framework to release real-time aggregate statistics under differential privacy based on filtering and adaptive sampling, improves the accuracy of released aggregates even under small privacy cost and can be used to enable a wide range of monitoring applications.
Journal ArticleDOI
A Basic Convergence Result for Particle Filtering
TL;DR: The basic nonlinear filtering problem for dynamical systems is considered, and a general framework, including many of the particle filter algorithms as special cases, is given.
Recursive Bayesian inference on stochastic differential equations
TL;DR: The main contributions of this thesis are to show how the recently developed discrete-time unscented Kalman filter, particle filter, and the corresponding smoothers can be applied in the continuous-discrete setting.
Journal ArticleDOI
Estimation of railway vehicle suspension parameters for condition monitoring
TL;DR: In this paper, a simplified plan view railway vehicle dynamical model is derived and a newly developed Rao-Blackwellized particle filter (RBPF) based method is used for parameter estimation.
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.