A Comparative Analysis of Solution Separation-based RAIM and Robust Cost Functions in Factor Graph Optimization for GNSS Positioning

Clark N. Taylor, Jason Gross

Peer Reviewed

Abstract:	Solution Separation-based RAIM has been developed and approved for safety-of-life applications, particularly in the aviation domain, enabling GPS-based positioning to return valid localization estimates with acceptable integrity assessments, even in the presence of outliers. On the other hand, many factor graph optimization techniques have been developed to perform optimization in the presence of a significant number of outliers. These techniques generally consist of using “robust cost functions” to make the optimization process less susceptible to outliers. In this paper, we directly compare the performance of solution separation-RAIM with some of the robust cost function - factor graph optimization (RCF-FGO) routines. We find that RCF-FGO techniques generally require less computation, have better alignment with true inlier/outlier assignments (as determined through simulation), and are more robust in high-outlier scenarios than the solution separation based RAIM algorithm. All code used for generating results is available at: https://github.com/cntaylor/FGO_gnss_integrity/
Published in:	Proceedings of the 2026 International Technical Meeting of The Institute of Navigation January 26 - 29, 2026 Hyatt Regency Orange County Anaheim, California
Pages:	302 - 311
Cite this article:	Taylor, Clark N., Gross, Jason, "A Comparative Analysis of Solution Separation-based RAIM and Robust Cost Functions in Factor Graph Optimization for GNSS Positioning," Proceedings of the 2026 International Technical Meeting of The Institute of Navigation, Anaheim, California, January 2026, pp. 302-311. https://doi.org/10.33012/2026.20495
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