Outlier-Robust Nonlinear Moving Horizon Estimation using Adaptive Loss Functions
This addresses outlier robustness in state estimation for control systems, representing an incremental improvement over existing robust estimation methods.
The paper tackles the problem of outlier contamination in nonlinear moving horizon estimation by proposing an adaptive robust loss function framework that prioritizes fitting uncontaminated data while downweighting outliers. Simulation results show adaptation occurs in just a few iterations while maintaining traditional L2 behavior with outlier-free measurements.
In this work, we propose an adaptive robust loss function framework for MHE, integrating an adaptive robust loss function to reduce the impact of outliers with a regularization term that avoids naive solutions. The proposed approach prioritizes the fitting of uncontaminated data and downweights the contaminated ones. A tuning parameter is incorporated into the framework to control the shape of the loss function for adjusting the estimator's robustness to outliers. The simulation results demonstrate that adaptation occurs in just a few iterations, whereas the traditional behaviour $\mathrm{L_2}$ predominates when the measurements are free of outliers.