NOVEL-APPROACH TO NONLINEAR NON-GAUSSIAN BAYESIAN STATE ESTIMATION

An algorithm, the bootstrap filter, is proposed for implementing recur sive Bayesian filters. The required density of the state vector is rep resented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linea rity or Gaussian noise: it may be applied to any state transition or m easurement model. A simulation example of the bearings only tracking p roblem is presented. This simulation includes schemes for improving th e efficiency of the basic algorithm. For this example, the performance of the bootstrap filter is greatly superior to the standard extended Kalman filter.