Global navigation satellite systems (GNSS) are vital for positioning autonomous vehicles, buses, drones, and outdoor robots. Yet its accuracy often degrades in dense urban areas due to signal blockage and reflections. Now, researchers have developed a GNSS-only method that delivers stable, accurate positioning without relying on fragile carrier-phase ambiguity resolution. Tested across six challenging urban scenarios, the approach consistently outperformed existing methods, enabling safer and more reliable autonomous navigation.
Accurately determining position is critical for the safety and reliability of autonomous vehicles and outdoor mobile robots. From self-driving cars and buses to delivery robots and drones, modern mobility systems depend on precise localization to make safe decisions in real time. While global navigation satellite systems (GNSS) provide absolute positioning on Earth, its performance often degrades in dense urban areas. Tall buildings, tunnels, and elevated structures frequently block, reflect, or absorb GNSS signals, causing unreliable positioning in urban canyons and complex city layouts.
In these environments, GNSS signals suffer from multipath propagation and non-line-of-sight (NLOS) interference. Reflected or obstructed signals distort measurements, challenging conventional high-precision approaches like real-time kinematic GNSS (RTK-GNSS), which rely on resolving carrier-phase integer ambiguities. This process is highly sensitive to signal interruptions and multipath effects. When ambiguity resolution fails, positioning errors can quickly grow from centimeters to meters, compromising safety-critical applications and highlighting the need for more robust urban positioning methods.
Addressing this challenge, a research team led by Associate Professor Junichi Meguro from the Department of Mechatronics Engineering, Meijo University in Japan, which included Mr. Daiki Niimi, a master’s student from the Division of Mechatronics Engineering, Meijo University, has developed a new GNSS-only positioning method specifically tailored for harsh urban conditions. This paper was made available online on December 8, 2025, and published in Volume 11, Issue 2 of the journal IEEE Robotics and Automation Letters on February 1, 2026.
The core innovation of this method is a tightly coupled Rao-Blackwellized particle filter that estimates position probabilistically without relying on carrier-phase integer ambiguity resolution. Instead of attempting to fix ambiguities—a major failure point for conventional RTK-GNSS in cities—the method evaluates the likelihood of multiple position hypotheses using the fractional component of carrier-phase measurements. This probabilistic framework enables stable positioning even when GNSS measurements are degraded due to multipath, partial signal blockage, or fluctuating satellite visibility, providing a reliable solution for autonomous systems operating in complex urban environments.
To enhance robustness further, the researchers integrated raw Doppler measurements into a Kalman filter, enabling consistent estimation of vehicle velocity and receiver clock drift over time. The method also implements particle-wise rejection of NLOS satellites and applies a robust filtering scheme based on Student’s t-distribution, which reduces the influence of outliers caused by multipath and other non-Gaussian errors. Collectively, these mechanisms allow the system to maintain stable position estimates even when only a few satellites are available, ensuring high reliability in difficult urban settings.
“We developed a GNSS-only positioning method for dense urban areas that remains accurate and stable under severe multipath,” highlights Mr. Niimi. “The key idea is a probabilistic approach that does not require carrier-phase integer ambiguity resolution, which is a major failure point for conventional RTK-GNSS in cities.”
The team validated the method using real-world vehicle data collected across six challenging urban environments in Nagoya and Tokyo. In five of the six test runs, the proposed approach outperformed existing GNSS-based methods, consistently achieving sub-meter accuracy despite severe satellite occlusion. In the most difficult scenario, the method exceeded the best conventional solution by nearly 30 percentage points, demonstrating its robustness in environments where traditional techniques frequently fail.
“GNSS is capable of directly estimating absolute position on Earth and is indispensable for autonomous driving and outdoor robotics,” adds Mr. Niimi. “The perception that GNSS is unreliable in cities stems not from the technology itself but from the limitations of conventional algorithms. This research shows that by fully leveraging GNSS measurements, stable and accurate urban positioning is achievable using GNSS alone.”
According to Dr. Meguro, the study tackles one of the most difficult problems in real-world satellite positioning. “Through careful experimental design and collaboration with external partners, we developed a new probabilistic approach that opens up new possibilities for satellite-navigation-only positioning,” he notes. “We expect these results to contribute significantly to the future of autonomous driving and outdoor autonomous mobile systems.”
Overall, this research demonstrates that high-accuracy, real-time GNSS positioning in dense urban environments is achievable without relying on fragile ambiguity resolution techniques. By improving robustness under severe signal degradation, the proposed method marks an important step toward safer and more dependable autonomous mobility systems operating in real-world city conditions.
About Meijo University
Meijo University traces its origin back to the establishment of the Nagoya Science and Technology Course in 1926, giving it a proud history of more than 90 years. As one of the largest universities in the Chubu region, Meijo University is a comprehensive learning institution that supports a wide range of academic fields from the humanities to physical sciences. With a network of more than 200,000 graduates and alumni, it strives to contribute not only to local industries but also to international communities in various fields. Meijo University is also known as the birthplace of the carbon nanotube. To foster the human resources of the next generation, the university continues to tackle ongoing challenges by further enhancing its campus and creating new faculties.
Website: https://www.meijo-u.ac.jp/english/
About Mr. Daiki Niimi from Meijo University
Mr. Daiki Niimi is a researcher and a master’s student in the Division of Mechatronics Engineering, Graduate School of Science and Technology at Meijo University, Japan. He specializes in robotics and advanced navigation systems, with a research focus on high-precision positioning for autonomous systems, including satellite-based navigation and integrated GNSS/IMU simulation methods. He is the recipient of the 15th Robotics Symposia Young Investigation Excellence Award (2025), recognizing his innovative contributions to the field. He has published peer-reviewed research papers on high-precision GNSS positioning and GNSS/IMU simulation, including work targeting robust urban GNSS localization. He is currently conducting research on GNSS odometry estimation methods using time-differenced carrier-phase (TDCP).
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