A group tracking algorithm for ground vehicle convoys
Wei C. Chin, Hian Beng Lee, Xu Xiao, Gee W. Ng, Khee Yin How · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
Tracking multiple ground targets under clutter and in real time poses several likely challenges: vehicles often get masked by foliage or line-of-sight (LOS) problems, manifesting in misdetections and false alarms. Further complications also arise when groups of vehicles merge or split. This paper presents an attempt to address these issues using a group tracking approach. Group tracking is a way to ameliorate, or at least soften the impact of such issues from the hope that at least partial information will be received from each target group even when the probability of detection, PD of each individual member is low. A Strongest Neighbour Association (SNA) method of measurement-to-track association based on space-time reasoning and track-measurement similarity measures has been derived. We combine the association strengths of the space-time dynamics, the degree-of-overlap and the historical affinity metrics to relate measurements and tracks. The state estimation is based on standard Kalman filter. Lastly, a Pairwise Historical Affinity Ratios (PHAR) is proposed for the detection of a split scenario. This method has been tested to work well on a simulated convoy-splitting scenario. Monte Carlo experiment runs of six different error rates with five different compositions of errors have been conducted to assess the performance of the tracker. Results indicated that the group tracker remains robust (>80% precision) even in the presence of high individual source track error rates of up to 30%.