IMM estimation for multitarget-multisensor air traffic surveillance

This paper deals with the design and implementation of an algorithm for track formation and maintenance in a multisensor Air Traffic Surveillance scenario. The major contribution of the present work is the development of the combined likelihood function that enables the replacement of the Kalman filter (KF) with the much more versatile interacting multiple model (IMM) estimator which, as a self adjusting variable-bandwidth state estimator accounts for the various motion modes of the aircraft. This likelihood function defines the objective function used in the measurement to track assignment algorithm. Also, this algorithm incorporates both skill and beacon returns i.e., it fuses the primary and secondary radar data. Data from two FAA radars are used to evaluate the performance of this algorithm. The use of the IMM estimator yields considerable noise reduction during uniform motion, while maintaining the accuracy of the state estimates during maneuver. Overall, the mean square prediction error (to the next observation time) is reduced by 30% and the rms errors in the altitude rate estimates are reduced by a factor of three over the KF. The usefulness of the tracker presented here is also demonstrated on a noncooperative target