Jean Dezert graduated from EFREI Engineering School in Paris in 1985, and obtained the Ph.D. degree in automatic control and signal processing from the University Paris XI, Orsay, France in 1990. During 1986– 1990, he did research in multi-sensor multi-target tracking (MS-MTT) at the French Aerospace Research Lab (ONERA), Châtillon, France. During 1991–1992, he visited the ESE Department at UConn University in Connecticut USA as Post-doc Research Fellow under supervision of Professor Bar-Shalom. During 1992–1993, he was a Teaching Assistant in Electrical Engineering Department at Orléans University, France. He joined ONERA in 1993, where he is a Maître de Recherches and Senior Research Scientist at ONERA, Palaiseau, France. His current research interests include estimation theory with applications to MS-MTT, information fusion, plausible reasoning under uncertainty, and decision-making support. He is a reviewer for several international journals. He has worked for the development of the International Society of Information Fusion (www.isif.org) and served in its Board. He was President of ISIF in 2016. Dr. Dezert has been involved in the Technical Program Committees of several International Conferences on Information Fusion. He gave several seminars, lectures, and tutorials on information fusion and tracking in Europe, USA, Canada, Australia, and China during the last three decades. He has published several book chapters, around 170 papers in conferences, and 90 papers in journals. He has also co-edited several books with Professor Smarandache on information fusion related to DSmT for reasoning about uncertainty and decision-making support.,Webpage: www.onera.fr/fr/staff/jean-dezert
In Dempster-Shafer evidence theory (DST), the determination of basic belief assignment (BBA) is an important yet challenging issue. The rational mass determination of compound focal elements is crucial for fully taking advantage of DST, i.e., the ability to represent the ambiguity. In this paper, for the compound focal elements, we select and construct the \enquote{compound-class samples} with ambiguous class membership. Then, we use these samples to construct an end-to-end model called Evidential Radial Basis Function Network (E-RBFN), with the input as the sample and the output as the corresponding BBA. The E-RBFN can directly determine the mass values for all focal elements (including the... More >
Graphical Abstract
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