Novel approach to nonlinear/non-Gaussian Bayesian state estimation
Gordon, N.J.
Salmond, D.J.
Smith, A.F.M.
Defence Res. Agency, Farnborough;
This paper appears in: Radar and Signal Processing, IEE Proceedings F
Publication Date: Apr 1993
Volume: 140,
Issue: 2
On page(s): 107-113
ISSN: 0956-375X
References Cited: 17
CODEN: IPFPEV
INSPEC Accession Number: 4397723
Current Version Published: 2002-08-06
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
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