Open AccessEvaluating the Reliability Coefficient of a Sensor Based on the Training Data Within the Framework of Evidence Theory
Author(s) -
Jingwei Zhu,
Xiaodan Wang,
Yafei Song
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2816915
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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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