Uncertain Data Aggregation in Classification and Tracking Processes
Alain Appriou · Studies in fuzziness and soft computing · 1998
To identify or localize a target, multisensor analysis has to be able to recognize one situation out of a set of possibilities. To do so, it uses measurements of more or less doubtful origin and prior knowledge that is understood to be often poorly defined, and whose validity is moreover difficult to evaluate under real observation conditions. The present synthesis proposes a generic modeling of this type of information, in the form of mass sets of the theory of evidence, with closer attention being paid to the most common case where the data originates from statistical processes. On the one hand robust target classification procedures can be achieved by applying appropriate decision criteria to these mass sets, on the other hand they can be integrated rigorously into a target tracking process, to reflect the origin of the localization measurements better. In all cases, the solutions found are placed in relation to those of the main competitive approaches currently used. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.