Application of Kalman Filtering on GNSS Receiver Signal Processing Techniques for Optimal Position Estimation

Abstract

Global Navigation Satellite System (GNSS) receiver signal processing techniques aimed at enhancing position estimation and tracking capabilities. This paper introduce the fundamental principles of GNSS technologies, including systems like GPS, GLONASS, and Galileo, and the critical challenges faced in signal processing. By delving into signal acquisition and pre-processing methods, their impacts on GNSS accuracy and reliability is measured using performance metrics. The core of research focuses on the application of Kalman filtering techniques, particularly the Extended Kalman Filter (EKF), for improved position estimation. compared to Kalman Filter (KF) and Least Square (LS) estimations. Ultimately, the findings highlight significant advancements and identify existing challenges in GNSS technology, paving the way for future research in signal processing and position estimation improvements.