%0 Electronic Article
%A N.J. Gordon
%A D.J. Salmond
%A A.F.M. Smith
%K state transition model
%K algorithm
%K bootstrap filter
%K recursive Bayesian filters
%K extended Kalman filter
%K simulation
%K measurement model
%K Gaussian noise
%K nonGaussian Bayesian state estimation
%K bearings only tracking problem
%K nonlinear Bayesian state estimation
%K state vector density
%K random samples
%X 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.
%@ 0956-375X
%T Novel approach to nonlinear/non-Gaussian Bayesian state estimation
%B IEE Proceedings F (Radar and Signal Processing)
%D April 1993
%V 140
%N 2
%P 107-113
%I
%U http://digital-library.theiet.org/;jsessionid=2gdvrugoggm6t.x-iet-live-01content/journals/10.1049/ip-f-2.1993.0015
%G EN