通信学报2016，Vol.37Issue(9)：20-29,10.DOI:10.11959/j.issn.1000-436x.2016174
基于区间证据理论的多传感器数据融合水质判断方法
Multi-sensor data fusion method for water quality evaluation based on interval evidence theory
摘要
Abstract
For the inevitable uncertainty and random uncertainty in the process of measuring water quality data with the sensor network, a multi-sensor data fusion method for water quality evaluation based on interval evidence theory was pro-posed. Considering the precision error of sensor and the abnormalities of measured data, every water quality data measured by sensor was represented by interval number. By calculating the distance between the water quality data and the features of each water quality class, the interval evidence of water quality class was acquired. According to the interval evidence com-bining rule, a comprehensive interval evidence was obtained by combining the interval evidence of each sensor. Finally, the water quality class was determined based on the comprehensive interval evidence by the decision rule. Experiments show that the proposed method can evaluate water quality class more accurately from the uncertain water quality data.关键词
水质判断/区间证据理论/传感网/多传感器数据融合/不确定性Key words
water quality evaluation/interval evidence theory/sensor network/multi-sensor data fusion/uncertainty分类
信息技术与安全科学引用本文复制引用
周剑,马晨昊,刘林峰,孙力娟,肖甫..基于区间证据理论的多传感器数据融合水质判断方法[J].通信学报,2016,37(9):20-29,10.基金项目
国家自然科学基金资助项目（No.71301081, No.61373139, No.61572261, No.61300165, No.61302157）；江苏省自然科学基金资助项目（No.BK20130877, No.BK20140895）；国家博士后基金资助项目（No.2014M551637）；江苏省博士后基金资助项目（No.1401046C）；南京邮电大学引进人才基金资助项目（No.NY213035）Foundation Items:The National Natural Science Foundation of China (No.71301081, No.61373139, No.61572261, No.61300165, No.61302157), The Natural Science Foundation of Jiangsu Province (No.BK20130877, No.BK20140895), Postdoctoral Science Foundation of China (No.2014M551637), Postdoctoral Science Foundation of Jiangsu Province (No.1401046C), The Scientific Re-search Foundation of Nanjing University of Posts and Telecommunications (No.NY213035) （No.71301081, No.61373139, No.61572261, No.61300165, No.61302157）
