摘要
针对传统数据融合方法节点抗干扰性较差、融合结果准确度低等问题,提出了一种基于物联网的农作物生长监测数据融合。对监测数据做预处理,去除监测数据中的粗大误差,提高数据融合的精准度;采用卡尔曼滤波算法建立滤波过程的状态空间模型,以此有效地减少数据采集时系统及传感器测量过程中产生的噪音,使系统当前状态估计值可以无限接近真实值;将所得的监测数据发送至簇头节点,实现基于状态补偿策略的加权数据融合。仿真结果表明,所提方法大幅提高了数据抗干扰能力,有效降低网络数据传输量和能耗,并且在数据融合精准度和融合时间方面具有较明显的优势。
Aiming at the problems of poor anti-interference and low accuracy of traditional data fusion methods,a data fusion method of crop growth monitoring based on Internet of things is proposed.Firstly,the monitoring data was preprocessed to remove the gross error in data and improve the accuracy of data fusion.Secondly,Kalman filter algorithm was used to build a state-space model of the filtering process,so as to effectively reduce the noise produced in the measurement process of system and sensor during the data acquisition.By this way,the current state estimation value of system can be infinitely close to the real value.Finally,the monitoring data were sent to the cluster head node,so that the weighted data fusion based on state compensation strategy was completed.Simulation results show that the proposed method greatly improves the anti-interference ability of data,effectively reduces the network data transmission and energy consumption and has obvious advantages in data fusion accuracy and fusion time.
作者
黄全高
HUANG Quan-gao(Gannan Normal University,Jiangxi Gannan 341000,China)
出处
《计算机仿真》
北大核心
2021年第7期381-384,414,共5页
Computer Simulation
关键词
物联网
数据融合
卡尔曼滤波
粗大误差
状态补偿
Internet of things
Data fusion
Kalman filter
Gross error
State compensation
