IMM ESTIMATION FOR MULTITARGET-MULTISENSOR AIR-TRAFFIC SURVEILLANCE

This paper deals with the design and implementation of an algorithm fo r track formation and maintenance in a multisensor Air Traffic Surveil lance scenario. The major contribution of the present work is the deve lopment of the combined likelihood function that enables the replaceme nt of the Kalman filter (KF) with the much more versatile interacting multiple model (IMM) estimator which, as a self adjusting variable-ban dwidth state estimator, accounts for the various motion modes of the a ircraft. This likelihood function defines the objective function used in the measurement to track assignment algorithm. Also, this algorithm incorporates both skin and beacon returns, i.e., it fuses the primary and secondary radar data. Data from two FAA radars are used to evalua te the performance of this algorithm. The use of the IMM estimator yie lds considerable noise reduction during uniform motion, while maintain ing accuracy of the state estimates during maneuver. Overall, the mean square prediction error (to the next observation time) is reduced by a factor of three over the KF. The usefulness of the tracker presented here is also demonstrated on a noncooperative target.