Data validation in the presence of stochastic and set-membership uncertainties

For systems suffering from different types of uncertainties, finding criteria for validating measurements can be challenging. In this paper, we regard both stochastic Gaussian noise with full or imprecise knowledge about correlations and unknown but bounded errors. The validation problems arising in the individual and combined cases are illustrated to convey different perspectives on the proposed conditions. Furthermore, hints are provided for the algorithmic implementation of the validation tests. Particular focus is put on ensuring a predefined lower bound for the probability of correctly classifying valid data.