Precision large scale air traffic surveillance using IMM/assignment estimators

We present the development and implementation of a multisensor-multitarget tracking algorithm for large scale air traffic surveillance based on intera cting multiple model (IMM) state estimation combined with a 2-dimensional a ssignment for data association, The algorithm can be used to track a large number of targets from measurements obtained with a large number of radars. The use of the algorithm is illustrated on measurements obtained from 5 FA A radars, which are asynchronous, heterogeneous, and geographically distrib uted over a large area. Both secondary radar data (beacon returns from coop erative targets) as well as primary radar data (skin returns from noncooper ative targets) are used. The target IDs from the beacon returns are not use d in the data association. The surveillance region includes about 800 targe ts that exhibit different types of motion, The performance of an IMM estima tor with linear motion models is compared with that of the Kalman filter (K F). A number of performance measures that can be used on real data without knowledge of the ground truth are presented for this purpose. It is shown t hat the IMM estimator performs better than the KF. The advantage of fusing multisensor data is quantified. It is also shown that the computational req uirements in the multisensor case are lower than in single sensor case. Fin ally, an IMM estimator with a nonlinear motion model (coordinated turn) is shown to further improve the performance during the maneuvering periods ove r the IMM with linear models.