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It is rather a challenge to find suitable strategies by which measurements 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 group of sensors into consensus is used. After each sensor in the group gathers information relevant to the sensory task, the group engages in what we call the uncertainty estimation stage. This is an information theorybased process that allows each sensor to assess its self\u2010uncertainty and the conditional uncertainties of other sensors. This process facilitates the computation of a weighting scheme that operates recursively on sensor observations until the group reaches a consensus. Whenever new observations are made, the uncertainty estimates of sensors are updated and used to compute a new weighting scheme. To demonstrate the efficacy and to show how the methodology works, the article discusses how the method can be used to tackle the multi\u2010sensor object identification problem. \u00a9 1993 John Wiley &amp; Sons, Inc.<\/jats:p>","DOI":"10.1002\/rob.4620100805","type":"journal-article","created":{"date-parts":[[2007,7,6]],"date-time":"2007-07-06T14:20:12Z","timestamp":1183731612000},"page":"1075-1093","source":"Crossref","is-referenced-by-count":9,"title":["New approach for aggregating multi\u2010sensory data"],"prefix":"10.1002","volume":"10","author":[{"given":"O. A.","family":"Basir","sequence":"first","affiliation":[]},{"given":"H. 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