基于广义置信度测度的多传感器数据融合
Multi-sensor data fusion based on a generalised belief divergence measure
摘要：
多传感器数据融合技术在实际应用中发挥着重要作用。由于证据理论在建模和处理不确定信息时不考虑先验概率的灵活性和有效性，因此在信息融合的各个领域得到了广泛的应用。然而，当融合高度冲突的证据时，可能会出现违反直觉的结果。为了解决这一问题，提出了一种基于广义信度测度的多传感器数据融合方法。该方法首先综合考虑证据的充分性和重要性，确定证据的可靠性权重;在此基础上，根据证据的可信度权重，设计了一种新的广义置信度Jensen-Shannon散度（GBJS）来度量多个证据之间的差异和冲突程度，并利用GBJS来度量证据的支持度。然后，在使用Dempster合成规则之前，先用证据的支持度对证据体进行调整。最后通过故障诊断实例验证了该方法的有效性。
英文摘要：
Multi-sensor data fusion technology plays an important role in real applications. Because of the flexibility and effectiveness in modelling and processing the uncertain information regardless of prior probabilities, Dempster-Shafer evidence theory is widely applied in a variety of fields of information fusion. However, counter-intuitive results may come out when fusing the highly conflicting evidences. In order to deal with this problem, a novel method for multi-sensor data fusion based on a new generalised belief divergence measure of evidences is proposed. Firstly, the reliability weights of evidences are determined by considering the sufficiency and importance of the evidences. After that, on account of the reliability weights of evidences, a new Generalised Belief Jensen-Shannon divergence (GBJS) is designed to measure the discrepancy and conflict degree among multiple evidences, which can be utilised to measure the support degrees of evidences. Afterwards, the support degrees of evidences are used to adjust the bodies of the evidences before using the Dempster's combination rule. Finally, an application in fault diagnosis demonstrates the validity of the proposed method.
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