传感器网络中基于预处理证据理论的数据融合

摘要

The recognition results of the same target by different sensors are often contradictory in wireless sensor networks. The use of data fusion based on Dempste-Shafer (D-S) evidence theory could solve this problem. However, when using D-S evidence combination formula to compute, with the increase of the target identity, the computation will be growing rapidly. The processing ability of sensor nodes is limited and the data of decision in sensor networks are redundant, thus, a way was proposed to reduce the number of target identity by preprocessing and to reduce the computation; and it could rule out the data with errors through greater consistency test; therefore, it makes decision results more accurate.%在无线传感器网络中,多个传感器节点对于同一个目标的识别结果经常会发生冲突.使用基于D-S证据理论的数据融合方案可以较好地解决这一问题.然而,采用D-S证据组合公式计算融合结果,随着可能的目标身份的增加,计算量会迅速增长.针对传感器节点有限的处理能力和节点的决策数据具有高冗余性的特点,提出通过预处理来减少计算时需要处理的目标身份的个数,减少了计算量;并通过一致性检验排除了误差较大的数据,从而使得决策结果更准确.