Resolution of Contradictory Sensor Data

Multisensor fusion is an important method for improving sensor reliability. Individual sensors are prone to transient errors, mechanical failures, and noise, as well as being of limited accuracy. It is, therefore, advisable to fuse readings from many heterogeneous sensors. Using a multitude of sensor technologies makes the overall system less sensitive to the failures of any one technology. Gleaning the best interpretation from a large number of partially contradictory sensor readings is not trivial. This paper presents an algorithm which finds the best interpretation of partially contradictory sensor readings, some of which are incorrect, that contain data of greater than two dimensions. Other algorithms return interpretations which are larger than optimal in order to avoid excessive computational complexity. The algorithm presented is based on data structures from computational geometry and provides the smallest possible region satisfying the constraints of the problem with a reasonable computational complexity.