The work herein details a final project for AA 273 (State Estimation and Filtering) at Stanford University, aimed at estimating a satellites attitude during a target tracking mission. The literature review explores current and emerging technologies, including tried-and-true sensors and advanced state estimation algorithms. This project offers insight into the best attitude estimation methods for satellite-based target tracking by implementing and comparing the performance of the following filters: Extended Kalman Filter (EKF), Multiplicative Extended Kalman Filter (MEKF), Unscented Kalman Filter (UKF), and Multiplicative Unscented Kalman Filter (MUKF). Through our work, it is shown that multiplicative techniques of estimating error offer superior accuracy with no increase in computational cost. It is also shown that the UKF and MUKF achieve better accuracy than the EKF and MEKF, respectively.