Adaptive interacting multiple model algorithm for tracking a manoeuvring target

Abstract

The paper describes an adaptive interacting multiple-model algorithm (AIMM) for use in manoeuvring target tracking. The algorithm does not need predefined models. A two-stage Kalman estimator is used to estimate the acceleration of the target. This acceleration value is then fed to the subfilters in an interacting multiple-model (IMM) algorithm, where the subfilters have different acceleration parameters. Results compare the performance of the AIMM algorithm with the IMM algorithm, using simulations of different manoeuvring-target scenarios. Also considered are the relative computational requirements, and the ease with which the algorithms can be implemented on parallel machines.