tailieunhanh - Sensor Fusion and its Applications Part 9

Tham khảo tài liệu 'sensor fusion and its applications part 9', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 234 Sensor Fusion and Its Applications If some of the detectors are imprecise the uncertainty can be quantified about an event by the maximum and minimum probabilities of that event. Maximum minimum probability of an event is the maximum minimum of all probabilities that are consistent with the available evidence. The process of asking an IDS about an uncertain variable is a random experiment whose outcome can be precise or imprecise. There is randomness because every time a different IDS observes the variable a different decision can be expected. The IDS can be precise and provide a single value or imprecise and provide an interval. Therefore if the information about uncertainty consists of intervals from multiple IDSs then there is uncertainty due to both imprecision and randomness. If all IDSs are precise then the pieces of evidence from these IDSs point precisely to specific values. In this case a probability distribution of the variable can be build. However if the IDSs provide intervals such a probability distribution cannot be build because it is not known as to what specific values of the random variables each piece of evidence supports. Also the additivity axiom of probability theory p A p A 1 is modified as m A m A m 0 1 in the case of evidence theory with uncertainty introduced by the term m 0 . m A is the mass assigned to A m A is the mass assigned to all other propositions that are not A in FoD and m 0 is the mass assigned to the union of all hypotheses when the detector is ignorant. This clearly explains the advantages of evidence theory in handling an uncertainty where the detector s joint probability distribution is not required. The equation Bel A Bel A 1 which is equivalent to Bel A Pl A holds for all subsets A of the FoD if and only if Bel s focal points are all singletons. In this case Bel is an additive probability distribution. Whether normalized or not the DS method satisfies the two axioms of combination 0 m A 1 and m A 1 . The third axiom m