For many real-world systems, which exhibit complex, nonlinear, and hybrid behavior, it is important to accurately track the state of these systems. The continuous state estimation problem has been well studied, and a number of extensions of the Kalman filter to nonlinear systems have been proposed. Hybrid state estimation poses an additional challenge, because the state model must be quickly updated during a mode change to facilitate accurate real time tracking. This paper discusses an approach to minimize the amount of equation regeneration necessary when the system's hybrid mode changes. These equations are used as the state update equation for an Unscented Kalman Filter (UKF), which is used to track the state of the system during a mode. We track the state of NASA's ADAPT test bench to show that the system presented here scales well for tracking large nonlinear and hybrid systems.