Reviewed by Lexie CornerApr 23 2025
A research team from Prince Sultan University presents a comprehensive analysis of UAV navigation in GPS-denied environments in a study published in the journal Satellite Navigation.
The image illustrates two different localization techniques for UAV navigation in GPS-denied environments: TERCOM (Terrain Contour Matching) on the left, which uses pre-mapped terrain data for absolute localization, and DSMAC (Digital Scene Matching Area Correlation) on the right, which compares real-time terrain data to a stored database. While TERCOM is effective in areas with distinguishable features, DSMAC provides more flexibility, particularly in dynamic and featureless environments. Image Credit: Satellite Navigation
Drone navigation in areas without reliable Global Positioning System (GPS) signals remains a significant challenge for modern aerospace technology. A recent study explores both absolute and relative localization strategies for Unmanned Aerial Vehicle (UAV) navigation in GPS-denied environments.
The study highlights the importance of hybrid approaches that combine multiple sensors and algorithms, as well as the potential of vision-based systems. In addition to advancing understanding of UAV navigation in complex environments, the work outlines a path for reliable, real-time operations in GPS-denied areas, which is crucial for applications such as autonomous delivery, surveillance, and disaster response.
UAV navigation typically relies on Global Navigation Satellite Systems (GNSS), such as GPS. However, in environments like indoors, urban canyons, or areas with signal blockage or jamming, GNSS becomes less effective.
Drones' dependence on GPS makes them vulnerable to interference from factors such as weather, tall buildings, and cyberattacks. While LiDAR and inertial sensors show promise as alternatives, they are often subject to drift and require high processing power.
Although vision-based and terrain-assisted systems still need further development to adapt to dynamic conditions, they may offer viable solutions. This underscores the need for robust multi-sensor fusion systems to enable safe and autonomous UAV operations in GPS-denied environments.
The study examines 132 publications, focusing on both absolute and relative localization methods, including vision-based approaches, LiDAR systems, and terrain-aided algorithms. The analysis of sensor fusion and computational efficiency reveals that hybrid approaches are the most reliable option for UAV navigation. This work provides valuable insights for real-world applications where GPS signals are unavailable, addressing key gaps in existing technologies.
The study identifies two main approaches to UAV navigation in GPS-denied environments: relative localization techniques such as SLAM (Simultaneous Localization and Mapping) and visual-inertial odometry, which depend on real-time sensor data, and absolute localization, which uses pre-mapped terrain data (e.g., TERCOM and DSMAC).
Relative methods offer greater flexibility but require significant processing power, while absolute techniques are limited in featureless environments. Despite challenges posed by lighting conditions, vision-based systems, particularly when augmented with AI for feature recognition, show considerable promise.
The study emphasizes the importance of sensor fusion, illustrating how integrating LiDAR, radar, and inertial measurements with advanced filtering methods like Kalman filters can greatly enhance navigation reliability.
Real-time processing is crucial, with hardware accelerators such as GPUs and optimized algorithms like LSTM networks enabling faster data analysis and decision-making.
While further development is needed to adapt these solutions for various environments, the study suggests that hybrid systems combining terrain maps with real-time SLAM data offer a balance between accuracy and flexibility.
The advancement of edge computing and AI processing capabilities will be critical for fully autonomous UAV operations in dynamic real-world conditions.
No single sensor or algorithm can solve all the challenges of GPS-denied navigation. Our research shows that combining absolute and relative localization with multi-sensor fusion is the key to achieving reliable UAV navigation. Future work must focus on optimizing these systems to handle the unpredictability of environments ranging from dense urban areas to remote disaster zones.
Dr. Imen Jarraya, Study Lead Author, Prince Sultan University
This research has significant implications for industries such as logistics, agriculture, and defense, which rely on UAVs. Military drones could operate in areas without a signal, and UAVs delivering medical supplies to remote or disaster-stricken areas could function without GPS.
To ensure the safe and effective integration of these technologies into future infrastructures, the study emphasizes the need for regulatory frameworks to standardize them. Addressing the limitations of GPS will enable safer and more efficient operations as UAVs become increasingly important in smart cities and infrastructure inspection.
The findings also support further investment in AI-powered navigation and collaborative research to improve these systems for broader applications.
Journal Reference:
Jarraya, I., et al. (2025). Gnss-denied unmanned aerial vehicle navigation: analyzing computational complexity, sensor fusion, and localization methodologies. Satellite Navigation. doi.org/10.1186/s43020-025-00162-z.