Real-time data fusion technique for validation of an autonomous system

Abstract

We describe a data fusion technique suitable for use in validation of a real-time autonomous system. The technique is based on the Dempster-Shafer theory and Murphy's rule for beliefs combination. The methodology is applied for fusing the learning stability estimates, provided by an online neural network monitoring methodology, into a single probabilistic learning stability measure. The case study shows that our data fusion technique is capable of handing real-time requirements and provides unique, meaningful results for interpreting the stability information provided by the online monitoring system.