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In this paper we compare the performances of centralized and distributed tracking architectures using a set of fighter aircraft scenarios. The tracking accuracy at platform (local) and global levels is evaluated fro track segments with uniform motion and different maneuvering scenarios. We evaluate the effects of target acceleration level, target separations, measurement accuracy, sensor revisit intervals and false alarm rates on the tracking performance at both local and global level. Kalman filter (KF) and Interacting Multiple Model (IMM) estimators with different target kinematic models are compared in terms of root mean square (RMS) position error, RMS velocity error and track purity. The computational requirements of different estimators are also compared. The centralized solution with perfect data association is used as a performance of benchmark for comparison. Scenarios considered include target maneuvers up to 3.5g and use measurements from up to 4 sensors on different platforms. Based on simulation results, appropriate estimator/data association options are recommended for different scenario configurations. An important conclusion is that, with the advent of the IMM estimator, the KF is obsolete for problems of this type. Also, the distributed estimator performs 10% worse than the centralized one.
Chen et al. (Thu,) studied this question.