Theory of belief functions for information combination and update in search and rescue operations

This paper presents a belief function approach for the location distribution of a search object in an optimal search planning context. We propose several ways to update the negative information obtained following an unsuccessful search mission using a belief functions framework. The discrete search space is defined by cells. We first represent the location information at the cell scale. We then generalize it to the complete grid. We compare different models for updating information on the search object location. We also suggest a way to take into account false alarms to test the expressive power of this framework and deal with a multi-sensor context.