复杂环境下基于支持度的多传感器数据融合算法

Data Fusion Algorithm Based on Support Degree under Interferential Environment

为避免融合算法依赖于先验信息,剔除复杂环境下多传感器测量数据中的干扰误差,一致性数据融合方法得到广泛研究。比较分析了典型的支持度度量方法,并构造了两种新的支持度度量方法。进而设计了一种新的基于传感器一致性和可靠性的融合算法,实现传感器的加权融合。仿真计算验证了新的支持度算法和融合算法的有效性。

Data fusion technology based on consensus is focused on avoiding the dependence of priori knowledge and telling apart the inaccurate measurement caused by interference. Here several classical method of measuring support degree are compared and two new ways to measure support degree are raised. Additionally a new weighted fusion algorithm is developed referring to the average of consensus degree and reliability of sensors. Simulation results shows that the new raised ways to measure support degree and fusion algorithm is effective.