This project investigates various techniques for estimating the state and identifying the type of Unmanned Aerial Systems (UAS) using a highly simplified radar model. The paper begins by developing and justifying the radar and UAS models, including their respective noise components. Next, we compare the performance of an Extended Kalman Filter (EKF) and Particle Filter for estimating system state accuracy. Finally, a multiple-model filter is designed to identify the likelihood that measurements correspond to each UAS type, providing a framework for UAS tracking and classification.