Tracking performance evaluation: track accuracy in dense target environments
Shozo Mori, Kuo‐Chu Chang, Chee-Yee Chong, Keh-Ping Dunn · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1990
ABSTRACT This paper is generally related to analytic methods for evaluating tracking performance, in par-ticular for predicting track accuracy in dense target environments. A very simple analytic expression is derived to predict the effects of mis-associations on track accuracy. The paper analyzes an optimal track-to-measurement assignment algorithm in track continuation phases, i.e., when tracks are well established. 1. INTRODUCTION This paper is concerned with multiple-sensor, multiple-target tracking in very high target density environments.In particular, we are interested in analytic methods for predicting the performance of tracking systems in such anenvironment. In [112 and [2] , a very simple analytic model was developed to predict the tracking performance in terms of2rack puriiy. The analysis was based on a performance prediction of an optimal track-to-measurement assignmentgiven the track/measurement accuracy and the target density on a given scan. Track purity was then estimated byapplying this prediction to multiple scans. To predict track accuracy for each scan, a Cramer-Rao type bound fora given mean (or ceniroid) trajectory was used. In other words, the effects of mis-association on the track accuracywas ignored. Although limited simulation results ([1]) showed the appropriateness of such an approach, it may bedesirable to have tighter bounds on track accuracy, and hence on track performance (purity). The main objectiveof this paper is to analyze the effects of mis-associations on the track accuracy, so that we may predict trackingperformance more precisely in terms of track purity estimation.The subject of predicting tracking performance is indeed as old as the emergence of the field of multi-target