A comparative study of TERCOM-EKF and ViSensorRF for UAV localization in GNSS-denied environments
Year: 2025
Type: article
Abstract: Accurate localization is essential for Unmanned Aerial Vehicles (UAVs) in mission-critical applications such as military operations, search and rescue, and environmental monitoring. While Global Navigation Satellite Systems (GNSS) provide reliable positioning in open environments, GNSS-denied conditions remain challenging, such as in obstructed or hostile environments. Various localization strategies have been developed using visual and/or non-visual sensors—such as LiDAR, barometers, Inertial Measurement Units (IMU), and cameras, with recent advances in machine learning. However, there is a lack of comparative studies that analyze the trade-offs between learning-based and conventional estimation approaches in such conditions. This study bridges that gap by comparing two fundamentally different methods: TERCOM-EKF, a Terrain Contour Matching algorithm-Extended Kalman Filter framework, and ViSensorRF, a machine learning approach based on Random Forest regression. Both methods are evaluated in a GNSS-denied simulation environment using PX4, Robot Operating System (ROS) 2, Gazebo, and QGroundControl over a digital elevation model of the Taif region in Saudi Arabia. Results demonstrate that ViSensorRF achieves superior localization accuracy, with a root mean square error (RMSE) of less than 7 meters, but requires significantly higher execution time of several seconds. In contrast, TERCOM-EKF offers faster computation of less than a second, making it suitable for real-time deployment in resource-constrained UAV systems. These findings highlight a trade-off between localization precision and computational efficiency, offering valuable insights for UAV deployment in GNSS-denied environments.
Source: Results in Engineering
Authors: Imen Jarraya, Khaled Gabr, Mohamed Abdelkader, Fatimah Alahmed, Muhammad Bilal Kadri, Wadii Boulila
Institution: Prince Sultan University
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,940
Funder: Prince Sultan University
OpenAlex ID: W4416794898