A New Method of Information Decision-making Based on D-S Evidence Theory

D-S evidence theory is a method broadly applied in fusion for decision-making. However, this theory has some shortcomings in the formula of evidence combination with the exception that evidence of fully conflict can not be combined, then the probability validity is difficult to determine and sometimes the composed evidence is different from people's subjective judgments or some other issues. These confine the application of evidence to some extent. Some of them have the dubious credibility which affects the fusion result when Multi evidence are combined together. In order to expand the application of the formula of this theory and enhance the reliability of the fusion results, a new combination formula is introduced in this paper, which is also compared with other formulas in other literatures and finally the improved reliability of this combination formula is verified. At last, through data-mining of the decision-making information on a number of isolated points, a new method using combined evidence to make decisions is described. It's proven from the experimental results that the new combination method not only works well and effectively in the evidence of a high level of conflict but also is applicable to fusion for decision-making

A Dempster-Shafer Method for Multi-Sensor Fusion

The Dempster-Shafer Theory, a generalization of the Bayesian theory, is based on the idea of belief and as such can handle ignorance. When all of the required information is available, many data fusion methods provide a solid approach. Yet, most do not have a good way of dealing with ignorance. In the absence of information, these methods must then make assumptions about the sensor data. However, the real data may not fit well within the assumed model. Consequently, the results are often unsatisfactory and inconsistent. The Dempster-Shafer Theory is not hindered by incomplete models or by the lack of prior information. Evidence is assigned based solely on what is known, and nothing is assumed. Hence, it can provide a fast and accurate means for multi-sensor fusion with ignorance. In this research, we apply the Dempster-Shafer Theory in target tracking and in gait analysis. We also discuss the Dempster-Shafer framework for fusing data from a Global Positioning System (GPS) and an Inertial Measurement Unit (IMU) sensor unit for precise local navigation. Within this application, we present solutions where GPS outages occur