无线传感器网络中基于区域决策的距离均值融合算法

A Mean Distance Fusion Algorithm Based on Region Decision in WSN

研究了无线传感器网络中基于距离的决策融合算法,并针对它们的不足,提出了一种基于区域决策的距离均值融合算法。该算法首先采用目标定位运算估计目标源的位置,并划定以目标源为中心的一定区域为有效区域,然后根据距离均值模型计算有效区域内传感节点的可信度权重,并将其决策和相应的权重上传到融合中心进行决策融合。仿真实验表明,该算法比其它基于距离的融合算法具有更高的检测概率和较好的抗噪性能,适合中大规模无线传感器网络。

Distance-based decision fusion algorithms in wireless sensor network （WSN） were researched, and a mean distance fusion algorithm based on region decision was reported. The location of detected target is estimated by the target location algorithm firstly, and a certain area using the detected target as center is regarded as the valid region. Then, the weights of the sensors in valid region are computed according to mean distance model. Also, the decision and the corresponding weights are transmitted to fusion center for decision fusing. Simulation results suggest that the proposed approach has higher detection probability and better anti-noisy performance compared to other distance based fusion algorithms, and it is suitable for moderate and large WSN.