Title :
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
Author :
Gordon, N.J. ; Salmond, D.J. ; Smith, A.F.M.
Author_Institution :
Defence Res. Agency, Farnborough, UK
fDate :
4/1/1993 12:00:00 AM
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 linearity or Gaussian noise: it may be applied to any state transition or measurement model. A simulation 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
Keywords :
Bayes methods; Kalman filters; filtering and prediction theory; state estimation; tracking; Gaussian noise; algorithm; bearings only tracking problem; bootstrap filter; extended Kalman filter; measurement model; nonGaussian Bayesian state estimation; nonlinear Bayesian state estimation; random samples; recursive Bayesian filters; simulation; state transition model; state vector density;
Journal_Title :
Radar and Signal Processing, IEE Proceedings F
