Data association through fusion of target track and identification sets
Erik Blasch, Lang Hong · 2000
A joint probability data association tracking algorithm typically associates only position measurements. With multiple-interacting targets in the presence of clutter, data association can be confused by spurious measurements. In this paper, we propose a set-based track and identification data association (SBDA) technique to leverage object identification information. We investigate the SBDA technique for a scenario in which a tracker has access to both coarse position measurements and belief identification information to enhance data association.