Novel approach to nonlinear/non-Gaussian Bayesian state estimation

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Novel approach to nonlinear/non-Gaussian Bayesian state estimation

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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.

Inspec keywords: Kalman filters; state estimation; filtering and prediction theory; Bayes methods; tracking

Other keywords: measurement model; nonlinear Bayesian state estimation; Gaussian noise; algorithm; bearings only tracking problem; bootstrap filter; nonGaussian Bayesian state estimation; random samples; simulation; extended Kalman filter; state vector density; state transition model; recursive Bayesian filters

Subjects: Simulation, modelling and identification; Signal processing and detection; Information theory; Other topics in statistics; Other topics in statistics

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