Association of tracks formed at different sensors is an ongoing area of interest in the field of information fusion and target tracking. In order to leverage additional information about a current target of interest that has been tracked at (an) additional sensor(s), track-to-track association (T2TA) must be performed. In addition to accurately identifying tracks with common origin, a desirable T2TA scheme will associate the tracks quickly, i.e., after only a few samples. A T2TA scheme is developed here that will take advantage of traditional kinematic state information as well as additional state information in the form of state augmentation. The main contribution is the use of two nonlinearly related state augmentations at the two sensors and accounting for their uncertainties. The results of T2TA are compared when using only kinematic state information, only state augmentation information, and the full augmented state. The full augmented state is shown to provide the most desirable association results, both in terms of accuracy and the number of samples needed to provide that accuracy.