Sensor Validation Using Dynamic Belief Networks

The trajectory of a robot is monitored in a restricted dynamic environment using light beam sensor data. We have a Dynamic Belief Network (DBN), based on a discrete model of the domain, which provides discrete mon­ itoring analogous to conventional quantita­ tive filter techniques. Sensor observations are added to the basic D BN in the form of specific evidence. However, sensor data is often par­ tially or totally incorrect. We show how the basic DBN, which infers only an impossible combination of evidence, may be modified to handle specific types of incorrect data which may occur in the domain. We then present an extension to the DBN, the addition of an invalidating node, which models the status of the sensor as working or defective. This node provides a qualitative explanation of incon­ sistent data: it is caused by a defective sen­ sor. The connection of success ive instances of the invalidating node models the status of a sensor over time, allowing the DBN to handle both persistent and intermittent faults.