The influx of unmanned aerial systems over the last decade has increased need for airspace awareness. Monitoring solutions such as drone detection, tracking, and classification become increasingly important to maintain compliance for regulatory and security purposes, as well as for recognizing aircraft that may not be so. Vision systems offer significant size, weight, power, and cost (SWaP-C) advantages, which motivates exploration of algorithms to further aid with monitoring performance. A method to classify aircraft using vision systems to measure their motion characteristics is explored. It builds on the assumption that at least continuous visual detection or at most visual tracking of an object of interest is already accomplished. Monocular vision is in part limited by range/scale ambiguity, where range and scale information of an object projected onto the image plane of a camera using a pin- hole model is generally lost. In an indirect effort to attempt to recover scale information via identity, classification of aircraft can aid in improvement of. These measured motion characteristics can then be used to classify the perceived object based on its unique motion profile over time, using signal classification techniques. The study is not limited to just unmanned aircraft, but includes full scale aircraft in the simulated dataset used to provide a representative set of aircraft scale and motion.