NEW APPROACH FOR AGGREGATING MULTISENSORY DATA

The task of sensory data fusion may involve the aggregation of sensory measurements that may be from different phenomenological domains and that, in many cases, could embrace some conflicting information cues. It is rather a challenge to find suitable strategies by which measurem ents obtained by the different sensors of the system can be aggregated so that a consistent interpretation of these measurements is achieved . In this article, we present a novel approach to achieve this goal. A recursive group utility function that is capable of bringing the grou p of sensors into consensus is used. After each sensor in the group ga thers information relevant to the sensory task, the group engages in w hat we call the uncertainty estimation stage. This is an information t heory-based process that allows each sensor to assess its self-uncerta inty and the conditional uncertainties of other sensors. This process facilitates the computation of a weighting scheme that operates recurs ively on sensor observations until the group reaches a consensus. When ever new observations are made, the uncertainty estimates of sensors a re updated and used to compute a new weighting scheme. To demonstrate the efficacy and to show how the methodology works, the article discus ses how the method can be used to tackle the multi-sensor object ident ification problem. (C) 1993 John Wiley and Sons, Inc.