Agents acting in physical space use perception in combination with their own world models and shared context models to orient. The shared context models have to be adapted permanently to the conditions of the real world. If a measurement of an agent’s sensor does not fit to the corresponding data in the shared context model the system contains an inconsistency. In this case it is necessary to decide whether the reason for the discrepancy is a change in the real world or a measurement error. If there is a change in the real world the shared context model has to be corrected. A single agent can hardly answer this question using its local world model. This work describes the scenario of a context model that is shared with and updated by many agents that possess one or more sensors. In occurrence of an inconsistency it is possible to call other agents to validate a measurement. The functions to call the other agents are provided by the Nexus platform, a federation of systems that manages users and objects in shared dynamic context models. The study reported in this paper evaluates procedures that combine a multitude of measurements to a single result that can be integrated in the shared context model. The statistically optimized procedure based on ratings of the participating agents is enhanced using scaled weighted arithmetic means which prevents the system from running into singularities caused by the feedback from the ratings. The method is combined with an additional preprocessing based on fuzzy clustering that detects aberrant measurements which can be excluded from further processing.