  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 monitoring analogous to
conventional quantitative filter techniques. Sensor observations are added to
the basic DBN in the form of specific evidence. However, sensor data is often
partially 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
inconsistent data: it is caused by a defective sensor. The connection of
successive instances of the invalidating node models the status of a sensor
over time, allowing the DBN to handle both persistent and intermittent faults.
