Performance prediction of feature aided track-to-track association

This paper is concerned with analytical and semi-analytical methods for predicting performance of track-to-track association, in terms of probability of each track being correctly associated with the track that shares the same origin, when association is performed by an optimal assignment algorithm. The focus of this paper is to quantify how much feature or attribute information can be expected to improve association performance over the usual track-to-track association using only kinematic or geolocational information. Our goal is to obtain a simple formula to predict the performance as a function of a set of key parameters that quantify the quality of feature information. The result extends the existing framework, which we may call the exponential law to predict association performance, to include the effects of the feature information.