TR2025-102
GNSS-RTK Factor Graph Optimization with Adaptive Ambiguity Noise
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- "GNSS-RTK Factor Graph Optimization with Adaptive Ambiguity Noise", American Control Conference (ACC), July 2025.BibTeX TR2025-102 PDF
- @inproceedings{Hu2025jul,
- author = {Hu, Yingjie and {Di Cairano}, Stefano and Berntorp, Karl},
- title = {{GNSS-RTK Factor Graph Optimization with Adaptive Ambiguity Noise}},
- booktitle = {American Control Conference (ACC)},
- year = 2025,
- month = jul,
- url = {https://www.merl.com/publications/TR2025-102}
- }
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- "GNSS-RTK Factor Graph Optimization with Adaptive Ambiguity Noise", American Control Conference (ACC), July 2025.
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Abstract:
This paper proposes a graph optimization-based real-time kinematic global navigation satellite system (GNSS) positioning approach, which consists of two stages of factor graph optimization (FGO). The first stage computes float solutions of navigation states including the carrier phase integer ambiguities, where we characterize the time evolution of integer ambiguities with an adaptive ambiguity model to accommodate cycle slips. By exploring the time-correlated constraint inherent in the integer ambiguity evolution, we achieve integer fixation with higher accuracy. The second-stage FGO takes the solutions from the first stage as prior and performs another graph optimization to obtain the fixed solutions of positions and velocities. Monte Carlo simulation results demonstrate that our proposed approach can achieve statistically smaller root mean square error in position estimates compared to Kalman filter- based method and is more robust to cycle slips.
Related News & Events
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NEWS MERL researchers present 13 papers at ACC 2025 Date: July 8, 2025 - July 10, 2025
Where: Denver, USA
MERL Contacts: Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; Purnanand Elango; Jordan Leung; Saviz Mowlavi; Diego Romeres; Abraham P. Vinod; Yebin Wang; Avishai Weiss
Research Areas: Control, Dynamical Systems, Electric Systems, Machine Learning, Multi-Physical Modeling, RoboticsBrief- MERL researchers presented 13 papers at the recently concluded American Control Conference (ACC) 2025 in Denver, USA. The papers covered a wide range of topics including Bayesian optimization for personalized medicine, machine learning for battery performance in eVTOLs, model predictive control for space and building systems, process systems engineering for sustainability, GNSS-RTK optimization, convex set manipulation, PDE control, servo system modeling, battery fault diagnosis, truck fleet coordination, interactive motion planning, and satellite station keeping. Additionally, MERL researchers (Vedang Deshpande and Ankush Chakrabarty) organized an invited session on design and optimization of energy systems.
As a sponsor of the conference, MERL maintained a booth for open discussions with researchers and students, and hosted a special session to discuss highlights of MERL research and work philosophy.
- MERL researchers presented 13 papers at the recently concluded American Control Conference (ACC) 2025 in Denver, USA. The papers covered a wide range of topics including Bayesian optimization for personalized medicine, machine learning for battery performance in eVTOLs, model predictive control for space and building systems, process systems engineering for sustainability, GNSS-RTK optimization, convex set manipulation, PDE control, servo system modeling, battery fault diagnosis, truck fleet coordination, interactive motion planning, and satellite station keeping. Additionally, MERL researchers (Vedang Deshpande and Ankush Chakrabarty) organized an invited session on design and optimization of energy systems.