摘要
采用RFID(radio frequency identification)技术在阅读器和动态电子标签之间进行通信,采集到的数据通常存在大量的脏数据。为更好支持高层应用,必须对原始数据进行清洗。针对标签频繁移动这一特点,将滑动窗口技术引入卡尔曼滤波模型,给出了一种改进的卡尔曼滤波模型,进而提出了一种基于改进卡尔曼滤波的RFID数据清洗方法。该方法在保证数据清洗准确率的基础上能有效解决标签动态跃迁带来的时间延迟问题,从而更加适用于标签频繁移动的场景。实验结果表明,该算法提高了清洗效率及准确率。
RFID （radio frequency identification） technology has been widely applied to the communication between RFID readers and dynamic electronic labels,but the data captured by RFID readers often tends to be noisy.In order to provide a better support for high-level RFID＇s applications,it is necessary to clean the collected data.Considering the characteristic of frequent movement of labels,this paper put forward an improved Kalman filter model by combining the sliding window technique with Kalman filter model and then proposed a method of RFID data cleaning based on an improved Kalman filter.The method can not only guarantee the accuracy of data cleaning but also effectively solve the problem of time delay caused by dynamic electronic tags.Thus this method is more adaptable to the situation where labels are moved frequently.The experiment＇s result shows this approach can improve the efficiency and accuracy of the data cleaning.
出处
《计算机科学》
CSCD
北大核心
2014年第3期202-204,227,共4页
Computer Science
基金
国防基础科研计划项目资助
