The degree of uncertainty in a track's position provides an indication of how best to allocate sensor resources. In this paper, we discuss the use of this track parameter for two tasking activities. First, we consider determining resolution selection for electro-optical (EO) sensor tasks. Secondly, we integrate our knowledge of kinematics into the process of information-theoretic task selection. We consider the use of optimized selection of the resolution for imaging sensors such that we maximize what we term the probability of acquiring a target. The probability of acquiring, if a particular resolution is chosen, is a function of both the probability of the target being within the sensor footprint as well as the probability the target is detected given it is in the footprint. In the process of selection of sensor tasks, Toyon employs an information-theoretic metric. We apply conditionals on the entropy that are a function of the uncertainty with which the track will actually be detected during the sensor task. A tracker, based on the Kalman filter, is used to provide an estimate of position and an associated two dimensional error covariance. The kinematic information is used to compute the probability a target is within a footprint, whose size is based on the resolution. For simulation we employ the high fidelity Toyon-developed SLAMEM testbed.