Journal ArticleDOI
Bayesian State Estimation for Tracking and Guidance Using the Bootstrap Filter
TLDR
A Monte Carlo simulation example of a bearings-only tracking problem is presented, and the performance of the bootstrap filter is compared with a standard Cartesian extended Kalman filter (EKF), a modified gain EKF, and a hybrid filter.Abstract:
The bootstrap filter is an algorithm for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples that are updated and propagated by the algorithm. The method is not restricted by assumptions of linearity or Gaussian noise: It may be applied to any state transition or measurement model. A Monte Carlo simulation example of a bearings-only tracking problem is presented, and the performance of the bootstrap filter is compared with a standard Cartesian extended Kalman filter (EKF), a modified gain EKF, and a hybrid filter. A preliminary investigation of an application of the bootstrap filter to an exoatmospheric engagement with non-Gaussian measurement errors is also given.read more
Citations
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BookDOI
Sequential Monte Carlo methods in practice
TL;DR: This book presents the first comprehensive treatment of Monte Carlo techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection.
Book
Sequential Monte Carlo methods for dynamic systems
TL;DR: A general framework for using Monte Carlo methods in dynamic systems and a general use of Rao-Blackwellization is proposed to improve performance and to compare different Monte Carlo procedures.
Journal ArticleDOI
An adaptive color-based particle filter
TL;DR: The integration of color distributions into particle filtering, which has typically been used in combination with edge-based image features, is presented, as they are robust to partial occlusion, are rotation and scale invariant and computationally efficient.
Journal ArticleDOI
Improved particle filter for nonlinear problems
TL;DR: In this article, a method of monitoring the efficiency of particle filters is introduced which provides a simple quantitative assessment of sample impoverishment and the authors show how to construct improved particle filters that are both structurally efficient in terms of preventing the collapse of the particle system and computationally efficient in their implementation.
Journal ArticleDOI
Gaussian particle filtering
J.H. Kotecha,Petar M. Djuric +1 more
TL;DR: It is shown that under theGaussianity assumption, the Gaussian particle filter is asymptotically optimal in the number of particles and, hence, has much-improved performance and versatility over other Gaussian filters, especially when nontrivial nonlinearities are present.
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 ArticleDOI
Non-Gaussian State—Space Modeling of Nonstationary Time Series
TL;DR: A non-Gaussian state—space approach to the modeling of nonstationary time series is shown, where the system noise and the observational noise are not necessarily Gaussian.
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.