Robust Sigma-Point Filtering for Nonlinear Systems with Non-Additive Noise

This paper addresses the problem of robust state estimation for nonlinear systems with non-additive noise. We build on ideas related to our previous work [1] by augmenting the sigma points with noise variables and embedding them into a minimax formulation. Specifically, the objective function is the variance of the state estimation error, the minimizer corresponds to the robust filter, and the maximizer represents the least favorable model within an ambiguity set about the nominal model. Simulation results demonstrate that the proposed filter achieves superior robustness and estimation accuracy compared to existing robust and standard sigma-point filters.