Unequal Dimension Track-to-Track Fusion Approaches Using Covariance Intersection

The fusion of tracks with different state spaces is referred to as heterogeneous track-to-track fusion (HT2TF). In this paper, we present novel approaches for HT2TF using Covariance Intersection (CI). The underlying idea is to augment the low dimensional track. We investigate whether the augmentation approaches proposed for mode mixing in the Interacting Multiple Model (IMM) algorithm can also be employed for the CI. As the augmentation influences the CI optimization, we compare different optimization variants. Finally, we evaluate the presented approaches for collective perception.