BAYESIAN STATE ESTIMATION FOR TRACKING AND GUIDANCE USING THE BOOTSTRAP FILTER

The bootstrap filter is an algorithm for implementing recursive Bayesi an filters, The required density of the state vector is represented as a set of random samples that are updated and propagated by the algori thm. The method is not restricted by assumptions of linearity or Gauss ian noise: It may be applied to any state transition of measurement mo del. A Monte Carlo simulation example of a bearings-only tracking prob lem is presented, and the performance of the bootstrap filter is compa red with a standard Cartesian extended Kalman filter (EKF), a modified gain EKF, and a hybrid fitter, A preliminary investigation of an appl ication of the bootstrap fitter to an exoatmospheric engagement with n on-Gaussian measurement errors is also given.