There are hosts of target tracking algorithm approaches, each valued with respect to the scenario operating conditions (e.g.
sensors, targets, and environments). Due to the application complexity, no algorithm is general enough to be widely
applicable, nor is a tailored algorithm able to meet variations in specific scenarios. Thus, to meet real world goals,
multitarget tracking (MTT) algorithms need to undergo performance assessment for (a) bounding performance over
various operating conditions, (b) managing expectations and applicability for user acceptance, and (c) understanding the
constraints and supporting information for reliable and robust performance. To meet these challenges, performance
assessment should strive for three goals: (1) challenge problem scenarios with a rich variety of operating conditions, (2) a
standard, but robust, set of metrics for evaluation, and (3) design of experiments for sensitivity analysis over parameter
variation of models, uncertainties, and measurements.