Robust Moving Horizon Estimation with Huber Loss for GNSS-Based Vehicle Localization under Outliers

Authors

Keywords:

moving horizon estimation, state-estimation, outliers, Huber-loss

Abstract

This work presents a robust state estimation strategy based on the Moving Horizon Estimation (MHE) framework, incorporating the Huber loss function (MHE-H) to improve its robustness to outliers. Traditional MHE often degrades its accuracy when measurements are disturbed by large deviations. To address this limitation, the MHE-H method replaces the quadratic penalty on measurement residuals with the Huber loss function, which preserves sensitivity to small errors while limiting the impact of outliers. The robustness and stability of the resulting estimator are analyzed, and guidelines for selecting the Huber parameter are presented. The approach is validated through two case studies: a nonlinear simulation example and an experimental dataset obtained from GNSS/IMU measurements with artificially injected outliers. Results demonstrate that MHE-H significantly reduces the estimation error compared to EKF and standard MHE, while maintaining an acceptable computational cost for real-time applications, making MHE-H a promising alternative for robust estimation in uncertain and noisy environments.

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Author Biographies

Hugo Sacha Uriel Hernández, Research institute for signals, systems and computational intelligence

Hugo S. U. Hernández is a Bioengineer and a PhD candidate at the Universidad Nacional del Litoral (UNL), Argentina, and a member of the Research Institute for Signals, Systems and Computational Intelligence (sinc(i) - CONICET). His research focuses on advanced state-estimation methods, particularly Moving Horizon Estimation, with applications to autonomous ground vehicle localization and multi-sensor fusion.

Agustín Capovilla, Research institute for signals, systems and computational intelligence

Agustín Capovilla is a Support Staff (CPA) member at the Research Institute for Signals, Systems and Computational Intelligence (sinc(i) - CONICET) and a Professor at the Universidad Nacional del Litoral (UNL). He is in the final year of a Computer Engineering degree at the Facultad de Ingeniería y Ciencias Hídricas, UNL. He is actively involved in robotics projects, specializing in the design and construction of autonomous vehicles, electronic circuit design, and embedded systems programming, as well as the implementation and integration of software for collaborative research initiatives.

Leonardo Luis Giovanini, Research institute for signals, systems and computational intelligence

Leonardo L. Giovanini is a Principal Researcher at the Research Institute for Signals, Systems and Computational Intelligence (sinc(i) - CONICET) and Professor at the Universidad Nacional del Litoral (UNL). He received his PhD in Engineering Science from UNL. His research interests focus on advanced modeling, optimal control, and estimation techniques for complex systems, with applications to networked systems, precision farming, and robotics.

Guido Marcelo Sánchez, Research institute for signals, systems and computational intelligence

Guido M. Sánchez is a Researcher at the Research Institute for Signals, Systems and Computational Intelligence (sinc(i) - CONICET) and an Associate Professor at the Universidad Nacional del Litoral (UNL). He received his PhD in Engineering from UNL. His research focuses on advanced automatic control systems and estimation, particularly for autonomous ground vehicles, including path-tracking, sensor fusion, nonlinear model predictive control and nonlinear moving horizon estimation.

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Published

2026-10-20

How to Cite

Hernández, H. S. U., Capovilla, A., Giovanini, L. L., & Sánchez, G. M. (2026). Robust Moving Horizon Estimation with Huber Loss for GNSS-Based Vehicle Localization under Outliers. IEEE Latin America Transactions, 24(11), 1320–1334. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/10917

Issue

Vol. 24 No. 11 (2026): Ordinary Issue

Section

Computing