Weighted Belief Entropy Based Conflict Measure and Fusion of Sensor Data
ZHOU Ying;TANG Yong-chuan;ZHAO Xiao-zhe;School of Electronics and Information,Northwestern Polytechnical University;
In target recognition,the sensor data is full of uncertainty. This paper proposes a sensor data fusion approach for target recognition based on weighted belief entropy in Dempster-Shafer evidence theory framework. Firstly,the uncertain information in the Frame of Discernment( FOD) is integrated into the Deng entropy model. Then,the weighted Deng entropy is applied to measure the uncertainty of the sensor data from different sources. Finally,the fusion of the conflicting data is implemented,based on which the decision-making on target recognition is realized. The rationality and effectiveness of the proposed method are validated by numerical simulations as well as the comparative experiments.
【CateGory Index】： TP212