Performance prediction of feature-aided track-to-track association

This paper describes analytic and semianalytic methods for predicting performance of track-to-track association, in terms of correct association probability, by an optimal assignment algorithm. The focus of this paper is to quantify how much feature or attribute information may improve association performance over the standard kinematic-only track-to-track association. Our goal is to obtain an analytical formula to predict the association performance as a function of a set of key parameters that quantify the quality of feature information. The result extends our previous development of an exponential law for predicting association performance, by including the effects of the additional generally non-Gaussian feature or attribute information.