A robust approach to sigma point Kalman filtering

Shenglun Yi; Mattia Zorzi

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arXiv · 2506.04815

A robust approach to sigma point Kalman filtering

Shenglun Yi·Mattia Zorzi

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Abstract

We propose a robust estimator for nonlinear state-space models and provide a clear interpretation of it as the minimizer of a minimax game. The corresponding maximizer searches for the least favorable model over an ambiguity set whose center is obtained by approximating the nominal model through a sigma-point transformation. Moreover, we develop a Markov Chain Monte Carlo (MCMC) scheme for generating adversarial data from it, thereby allowing the assessment of the resulting uncertainty.

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Shenglun Yi, Mattia Zorzi. 2025-06-05. A robust approach to sigma point Kalman filtering. https://arxiv.org/abs/2506.04815

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