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 http://dx.doi.org/10.1049/ip-f-2.1993.0015

UL https://digital-library.theiet.org/;jsessionid=1g7ubyix0cb0k.x-iet-live-01content/journals/10.1049/ip-f-2.1993.0015

LA English

SN 0956-375X

YR 1993

OL EN