A Fault Tolerant Architecture for Data Fusion Targeting Hardware and Software Faults

This paper presents a fault tolerance architecture for data fusion mechanisms that tolerates hardware faults in the sensors and software faults in the data fusion. After introducing the basic concepts of fault tolerance and data fusion, we present first the generic architecture before detailing an implementation using Kalman filters for mobile robot localization. Finally fault injection is used on real data from this implementation to validate our architecture. Under a single fault hypothesis, we detect hardware and software faults and recover from hardware faults. With more redundancies, it would be possible to consider multiple faults and recover from software ones.