期刊
IEEE ACCESS
卷 6, 期 -, 页码 30592-30601出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2816915
关键词
Classification problems; evidence theory; reliability coefficient; sensor; training data
资金
- National Natural Science Foundation of China [61273275, 60975026, 61703426, 61503407]
When a multi-sensor data fusion system is used to handle a classification problem, it is necessary to incorporate the reliability coefficients of all the sensors into the fusion process. Within the framework of evidence theory, this paper proposes a new method for evaluating the reliability coefficient of a sensor based on the training data. In this method, the distance between power-set-distribution betting commitments is used to quantify the dissimilarity between the sensor reading and the reality, which can be served as a one-sided discounting factor. Then, the optimization approach is put forward to obtain an all-sided discounting factor from plenty of one-sided discounting factors. The advantages of the proposed method are analyzed comparatively. Numerical examples are also presented to demonstrate its performance by comparing it with other supervised evaluation methods.
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