Optimizing GPS Kinematic Accuracy by Employing the Kalman Filter Technique, Case Study: Mecca - Medina Highway
Received: 10 February 2025 | Revised: 27 February 2025 | Accepted: 9 March 2025 | Online: 22 March 2025
Corresponding author: Medhat M. Helal
Abstract
This study presents an analysis of the feasibility of integrating a Kalman filter model with GPS kinematic data to improve position accuracy. The focus is on estimating the receiver's coordinates for a set of points using pseudo-range measurements from a single handheld GPS receiver. GPS signal errors compromise the accuracy of positioning, and a Kalman filter is utilized to improve the kinematic positioning of GPS points using a single-frequency I-COM GP 22 handheld receiver along a 180 km segment of the Mecca-Medina highway in Saudi Arabia to establish an accurate layer of the highway in a GIS system. A Kalman filter allowed characterizing noise sources and reducing their impact on the receiver's output. The core of the GPS-Kalman filter was a model that describes how the state vector evolves over time. As a recursive estimator, the Kalman filter provides the minimum covariance estimate of the state vector by processing and weighting measurements relative to the model's assumptions. The numerical results demonstrate that the RMS of the observed data before filtering was 4.2 m in the east direction and 3.7 m in the north direction. After applying the Kalman filter model, the RMS values decreased to 1.4 m in the east and 1.2 m in the north. The Kalman filter technique is a valuable tool for enhancing dynamic process analysis.
Keywords:
GPS, kinematic measurements, Kalman filter, errors, position accuracy, navigationDownloads
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Copyright (c) 2025 Tarek A. Eldamaty, Medhat M. Helal
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