Traditional approaches to persistent surveillance generate prodigious amounts of data, stressing storage, communication, and analysis systems. As such, they are well suited for compressed sensing (CS) concepts. Existing demonstrations of compressive target tracking have utilized time-sequences of random patterns, an approach that is sub-optimal for real world dynamic scenes. We have been investigating an alternative architecture that we term SCOUT-the Static Computational Optical Undersampled Tracker-which uses a pair of static masks and a defocused detector to acquire a small number of measurements in parallel. We will report on our working prototypes that have demonstrated successful target tracking at 16x compression.