OICC Press Majlesi Journal of Electrical Engineering 2345-3796 2026 11 06 Enhanced Navigation Accuracy Using Nonlinear Kalman Filters in INS/GPS Integration 10.57647/j.mjee.2025.16846 EN Mohammadreza Mollaei Department of Electrical and Computer Engineering, University of Kashan, Kashan, Iran Mohsen Shafieirad Department of Electrical and Computer Engineering, University of Kashan, Kashan, Iran https://orcid.org/0000-0002-3239-8987 Elahe Moradi Department of Electrical Engineering, YI.C., Islamic Azad University, Tehran, Iran Journal Article 2026 11 06 This study explores the challenges of error accumulation in inertial navigation systems (INS) and presents a solution to enhance navigation accuracy. To address these errors, INS data is integrated with GPS using advanced nonlinear Kalman filters, specifically the Unscented Kalman Filter (UKF) and the Particle Kalman Filter (PKF). These methods are applied to a six-degree-of-freedom fixed-wing aircraft model, and their performance is evaluated under both GPS-enabled and GPS-denied conditions. The results show that nonlinear filters, particularly the PKF, outperform the Extended Kalman Filter (EKF) in providing accurate position and velocity estimates, while also preventing system divergence during GPS outages. This study confirms that integrating INS and GPS with advanced nonlinear filters can significantly enhance navigation accuracy and reliability.