基于Mamdani型模糊推理的加权质心定位算法  

Weighted Centroid Localization Algorithm Based on Mamdani Fuzzy Theory

作　　者：王万良[1] 石浩[1] 李燕君[1] 

机构地区：[1]浙江工业大学计算机科学与技术学院,杭州310023

出　　处：《计算机科学》2015年第10期101-105,121,共6页Computer Science

基　　金："十二五"国家科技支撑计划(2012BAD10B01)资助

摘　　要：为了提高加权质心定位算法在室内环境中的定位精度,提出使用实际环境中的RSS数据通过蝙蝠算法拟合输入隶属度函数,通过Mamdani型模糊推理获得节点间精确的权值,以提高加权质心定位算法的定位精度。在Zigbee平台上实现了该算法,通过实验比较3种不同的质心定位算法,结果表明:Mamdani型模糊推理因采用经过蝙蝠算法优化的隶属度函数而具有更小的平均定位误差。In many cases of wireless sensor networks application, the accuracy of weighted centroid localization algo- rithm depends on the precision of weight. In this paper, Mamdani fuzzy logic inference approach with improved RSS membership function based on bat algorithm was proposed to improve the accuracy of weighted centroid localization al- gorithm. With applying Zigbee hardware platform to compare three types of centroid algorithms, it draws a conclusion that the desired accuracy in door localization can be achieved with optimized RSS membership function by bat algo- rithm.

关 键 词：加权质心定位算法 模糊推理 蝙蝠算法 接收信号强度 

分 类 号：TN393[电子电信—物理电子学]