This document presents a simulation of target tracking using an unscented Kalman filter (UKF) implemented in Simulink. It begins with an introduction to nonlinear state estimation and the UKF as an extension of the extended Kalman filter. It then describes implementing a model of random aircraft motion and measurements in Simulink. The UKF algorithm is explained as using sigma points to capture the mean and covariance of distributions propagated through nonlinear transformations more accurately than the EKF. Simulation results show the estimated trajectory closely matching the actual trajectory with decreasing errors over time. The UKF is concluded to provide better estimation performance than other filters like the KF and EKF, while the Simulink implementation allows for real-time applications on DSP