RT Journal Article
A1 N.J. Gordon
A1 D.J. Salmond
A1 A.F.M. Smith

PB
T1 Novel approach to nonlinear/non-Gaussian Bayesian state estimation
JN IEE Proceedings F (Radar and Signal Processing)
VO 140
IS 2
SP 107
OP 113
AB 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.
K1 state transition model
K1 algorithm
K1 bootstrap filter
K1 recursive Bayesian filters
K1 extended Kalman filter
K1 simulation
K1 measurement model
K1 Gaussian noise
K1 nonGaussian Bayesian state estimation
K1 bearings only tracking problem
K1 nonlinear Bayesian state estimation
K1 state vector density
K1 random samples
DO https://doi.org/10.1049/ip-f-2.1993.0015
UL https://digital-library.theiet.org/;jsessionid=3daufp40deql4.x-iet-live-01content/journals/10.1049/ip-f-2.1993.0015
LA English
SN 0956-375X
YR 1993
OL EN