Next generation instrumentation systems for live military training will simulate weapon engagements (shots) using geo-pairing: matching shooter with target by geometric computations that determine whether the trajectory or blast of the round intersects the target. Such calculations depend critically on accurate sensor readings, including positions of shooter and target(s), the pointing angle of the weapon at the time of the simulated shot, and positions of terrain obstacles. However, perfect sensors are unattainable, so a key question for system engineers is just how accurate do the sensors need to be? After all, physical weapons themselves have inherent inaccuracies (weapon "dispersion" or "spread"); for training purposes, it is not necessary to simulate better accuracy than exists in the weapons themselves. Moreover, a less accurate point angle sensor can to some degree be compensated for by a more accurate position sensor, and vice versa. Thus, ultimately we would like to understand this tradeoff quantitatively in order to support cost effective system engineering decisions.

This paper describes an iterative statistical approximation method, implemented as a set of computational tools, used by the U.S.Army's One Tactical Engagement Simulation System (OneTESS) Project for supporting these design tradeoffs. In particular, we show how to compute the probability of correct sensor-based shot adjudication for given sensor combinations, as well as tradeoff diagrams showing which combinations of sensors can be used together to achieve realism matching the physical accuracy of actual weapons systems. A key part of this method is building approximate computational models of aim, weapon spread, and sensor error distributions based upon input gleaned from subject matter experts in the areas of live training and instrumentation. We illustrate the method with case studies involving direct fire, indirect fire, and terrain problems.