It is necessary to take the information quality into consideration in information fusion. However, existing information quality has a limitation due to the ignorance of the credibility of information source. In this paper, the generalized information quality (GIQ) been proposed considering the association among the collected sensor reports, first. Then, the ordered weighting aggregation (OWA) operator of probability distribution based on the GIQ is presented. Numerical example and real application in fault diagnosis are used to illustrate the efficiency of the proposed method. The proposed GIQ and OWA algorithm has the advantage in fault tolerance and reduce the impact of conflict data in data fusion process, due to the consideration of the credibility of each sensor report. The proposed method has the promising aspects in uncertainty data aggregation.

Bayesian update is widely used in data fusion. However, the information quality is not taken into consideration in classical Bayesian update method. In this paper, a new Bayesian update with information quality under the framework of evidence theory is proposed. First, the discounting coefficient is determined by information quality. Second, the prior probability distribution is discounted as basic probability assignment. Third, the basic probability assignments from different sources can be combined with Dempster's combination rule to obtain the fusion result. Finally, with the aid of pignistic probability transformation, the combination result is converted to posterior probability distribution. A numerical example and a real application in target recognition show the efficiency of the proposed method. The proposed method can be seen as the generalized Bayesian update. If the information quality is not considered, the proposed method degenerates to the classical Bayesian update.