Unscented-Kalman-Filter-Based Multitarget Tracking Algorithms for Airborne Surveillance Application

Abstract

This paper addresses the problem of multitarget tracking for aerial surveillance application. An infrared search and track sensor, mounted on an unmanned platform, is deployed for tracking and provides three-dimensional bearing and elevation angle measurements immersed with clutters. A Gaussian-mixture/probability-hypothesis-density multitarget tracker using an unscented Kalman filter is proposed to estimate target trajectories. A new closed-loop formulation of the unscented-Kalman-filter/Gaussian-mixture/probability hypothesis density is derived using fundamental properties of nonlinear Bayesian filtering. A tag management procedure to maintain individual track identity is also discussed. The performance of the unscented-Kalman-filter/Gaussian-mixture/probability hypothesis density is compared with seven data-association-based multitarget tracking algorithms, such as 1) the suboptimal nearest neighbor, 2) the global nearest neighbor using Munkres optimal assignment method, 3) the global nearest neighbor with the Jonker–Volgenant–Castanon assignment technique, 4) the multihypothesis tracking using -best feasible solutions, 5) probabilistic data association, 6) nearest-neighbor probabilistic data association, and 7) -nearest-neighbor probabilistic data association. These algorithms are computed using the unscented Kalman filter and follow a track-oriented approach. Simulation results reveal that the unscented-Kalman-filter/Gaussian-mixture/probability hypothesis density algorithm has superior performance over conventional data-association methods in terms of cardinality accuracy and robustness.