Bayesian state estimation for tracking and guidance using the bootstrap filter

1 November 1995

journal article
research article
Published by American Institute of Aeronautics and Astronautics (AIAA) in Journal of Guidance, Control, and Dynamics

Vol. 18 (6) , 1434-1443
https://doi.org/10.2514/3.21565

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 of 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 fitter, A preliminary investigation of an application of the bootstrap fitter to an exoatmospheric engagement with non-Gaussian measurement errors is also given.

This publication has 11 references indexed in Scilit:

Novel approach to nonlinear/non-Gaussian Bayesian state estimation
IEE Proceedings F Radar and Signal Processing, 1993
A Monte Carlo Approach to Nonnormal and Nonlinear State-Space Modeling
Journal of the American Statistical Association, 1992
Monte Carlo integration in general dynamic models
Contemporary Mathematics, 1991
Recursive Bayesian estimation using piece-wise constant approximations
Automatica, 1988
A stochastic analysis of a modified gain extended Kalman filter with applications to estimation with bearings only measurements
IEEE Transactions on Automatic Control, 1985
Utilization of modified polar coordinates for bearings-only tracking
IEEE Transactions on Automatic Control, 1983
Approximate non-Gaussian filtering with linear state and observation relations
IEEE Transactions on Automatic Control, 1975
Nonlinear Bayesian estimation using Gaussian sum approximations
IEEE Transactions on Automatic Control, 1972
A Bayesian approach to problems in stochastic estimation and control
IEEE Transactions on Automatic Control, 1964
Density Estimation for Statistics and Data Analysis
Published by Springer Nature ,1400