GCI fusion based multi-detection multitarget tracking
Lin Gao, Giorgio Battistelli, Luigi Chisci, Alfonso Maria Farina · 2020
Multi-detection (MD) observation systems are characterized by multiple observation modes (OMs) and thus simultaneously generate multiple measurements for each target. The main difficulty of exploiting MD systems for multitarget tracking (MTT), in contrast to single-detection (SD) systems, is the great amount of extra computational resources required in order to solve the resulting multidimensional assignment problem. This paper proposes a novel computationally efficient MTT approach for MD systems, wherein a bank of OM-dependent MTT filters with SD model are employed and the OM-dependent posteriors are then fused based on the well-known generalized covariance intersection (GCI) rule. In this way, the computational complexity is significantly reduced compared to existing MTT algorithms with MD model. The effectiveness of the proposed algorithm is assessed by simulation experiments.