基于NB-IoT环境监测的多传感器数据融合技术  

Multi-Sensor Data Fusion Technology Based on NB-IoT Environment Monitoring

作　　者：聂珲 陈海峰 NIE Hui;CHEN Haifeng(School of Electronic Engineering,Xi’an University of Posts and Telecommunications,Xi’an 710121,China)

机构地区：[1]西安邮电大学电子工程学院,西安710121

出　　处：《传感技术学报》2020年第1期144-152,共9页Chinese Journal of Sensors and Actuators

基　　金：国家自然科学基金项目(61306131)。

摘　　要：为了有效的对环境质量进行综合评价,结合窄带物联网(NB-IoT)技术和传感器技术构建的环境监测系统,对采集的温度、湿度、甲醛、粉尘(PM2.5)和总挥发有机化合物(TVOC)等环境因素进行多传感器数据融合研究。采用两级并联型融合方式对环境质量进行评价,融合前对采集的数据进行中值滤波剔除因外界干扰产生的异常数据;其次利用卡尔曼滤波算法对多组同类传感器数据融合,得到最佳的同类传感器值;最后运用模糊综合评价法将上一级融合后的各异类传感器进行决策层融合,其中的权重值由熵值法确定,隶属度函数采用高斯型。运用上述算法分别对不同环境场景进行测试,仿真结果表明通过多传感器数据融合能够获取更加丰富且有效的环境信息,消除单因子传感器对环境质量评价的简单性和局限性,提高整体环境质量评价的可靠性与准确性。In order to make an effective comprehensive evaluation of environmental quality,the environmental monitoring system built by NB-IoT technology and sensor technology is used to conduct multi-sensor data fusion research on the collected environmental factors such as temperature,humidity,formaldehyde,PM2.5 and TVOC.In the same time,the two-level parallel fusion method is adopted to evaluate the environmental quality.Before data fusion,median filtering is used to eliminate abnormal data.Then,the Kalman filter algorithm is used to fuse multiple sets of similar sensors to obtain the best value of it.Finally,the fuzzy comprehensive evaluation method is used to fuse the different types of sensors at the decision-making level,in which the weight value is determined by the entropy method and the membership function is Gaussian.The different environment scenarios are tested by using the above algorithms,and the simulation results show that multi-sensor data fusion can obtain more abundant and effective environmental information,overcome the simplicity and limitation of single-factor sensor for environmental quality assessment,and improve the reliability and accuracy of the overall environmental quality assessment.

关 键 词：NB-IoT 并联型融合 模糊综合评价 卡尔曼滤波 熵值法 

分 类 号：X830.2[环境科学与工程—环境工程]