Multiple model estimation using the bootstrap filter

The use of multiple models, each matched to a different hypothetical target motion, has been shown to be a highly effective approach to tracking a manoeuvring target. This article proposes a new extension to the bootstrap filter, a sample based algorithm for recursive Bayesian estimation, for application to the multiple model problem. It is shown that, by using a more general estimator than the Kalman filter, the true model conditioned densities can be propagated and the number of estimators, and therefore the computational load, in the multiple model system can be kept constant, equal to the number of models. A further distinct advantage of this approach is that the multiple model bootstrap filter is directly applicable to nonlinear and non-Gaussian multiple model systems. Simulation results comparing this technique with the IMM algorithm using standard manoeuvring target scenarios are presented using both Cartesian and polar co-ordinates. In the Cartesian case the target model is linear and comparable performance to IMM is achieved. In the polar case the target model is now nonlinear. Good tracking is observed with the multiple model bootstrap filter whereas the IMM implemented using EKFs displays poor adaption to manoeuvres