Accurate position estimation is vital for the effective operation of autonomous systems, particularly in mobile robotics and autonomous vehicles. Traditional estimation methods, such as the Extended Kalman Filter (EKF), face significant challenges in handling nonlinearities, often leading to errors or instability. To address these limitations, this paper introduces the Scaled Unscented Kalman Filter (SUKF), which utilizes the Unscented Transform (UT) to better approximate the mean and covariance of state variables. Unlike the EKF, which relies on linearization, the SUKF captures nonlinear dynamics with greater precision. This study outlines a practical implementation of the SUKF in MATLAB, combining GPS and IMU data from the Xsens MTI G-710 sensor. The process involves synchronizing sensor data, characterizing noise profiles, and developing a robust process model for navigation. Through simulations and real-world experiments, the SUKF is shown to significantly improve localization accuracy compared to EKF-based methods, achieving error reductions of over 60%, as reported in the literature. These findings highlight the SUKF’s reliability and robustness, making it an excellent choice for real-time applications in mobile robotics and vehicular navigation. This work demonstrates that the SUKF not only addresses the shortcomings of existing methods but also offers a practical pathway for achieving high-precision localization in complex environments.