计算机科学 ›› 2019, Vol. 46 ›› Issue (4): 100-105.doi: 10.11896/j.issn.1002-137X.2019.04.016

• 网络与通信 • 上一篇    下一篇

RFID环境下基于自适应卡尔曼滤波的高速移动车辆速度预测

冯安琪, 钱丽萍, 黄玉蘋, 吴远   

  1. 浙江工业大学信息工程学院 杭州310023
  • 收稿日期:2018-02-06 出版日期:2019-04-15 发布日期:2019-04-23
  • 通讯作者: 钱丽萍(1981-),女,博士,教授,CCF会员,主要研究方向为无线通信、深空通信、认知无线电网络、智能电网,E-mail:lpqian@zjut.edu.cn(通信作者)
  • 作者简介:冯安琪(1995-),女,硕士生,主要研究方向为网络与智能系统,E-mail:aqfeng_zjut@163.com;黄玉蘋(1995-),男,硕士生,主要研究方向为网络与智能系统;吴 远(1981-),男,博士,教授,CCF会员,主要研究方向为无线网络、网络资源优化管理、网络安全。
  • 基金资助:
    本文受国家自然科学基金(61379122),浙江省自然科学基金(LR16F010003,LR17F010002)资助。

RFID Data-driven Vehicle Speed Prediction Using Adaptive Kalman Filter

FENG An-qi, QIAN Li-ping, HUANG Yu-pin, WU Yuan   

  1. College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China
  • Received:2018-02-06 Online:2019-04-15 Published:2019-04-23

摘要: 针对高速移动车辆的速度预测问题,提出了一种射频识别(Radio Frequency Identification,RFID)环境下的基于自适应卡尔曼滤波的车辆速度预测方法。在RFID系统中,当车辆通过标签时,首先,阅读器需要获取该标签上最后一辆车的状态信息(即当前速度和时间戳),同时将自己的状态信息发送到该标签;然后,根据所获得的状态信息来构造状态空间模型;最后,通过带有变遗忘因子的自适应卡尔曼滤波算法来预测和调整车速。自适应卡尔曼滤波算法是利用期望输出值与实际输出值之间的误差来实现自适应遗忘因子的自适应更新,从而实现预测模型的在线更新。数值结果进一步表明,与最小二乘法和传统的卡尔曼滤波算法相比,该算法分别提高了87.5%和50%的速度预测精度,从而证明该算法可以为实际应用提供更好的实时性。

关键词: 速度预测, 射频识别, 数据采集, 自适应, 卡尔曼滤波

Abstract: This paper proposed a radio frequency identification (RFID) data-driven vehicle speed prediction method using adaptive Kalman filter.First of all,when the vehicle moves through one RFID tag,the reader needs to acquire the state information (i.e.,current speed and time stamp) of the last vehicle across this tag,meanwhile transmits its own state information to this tag.Then,the state space model can be formulated according to the acquired state information.Finally,the adaptive Kalman filtering algorithm is used to predict and adjust the vehicle speed.Adaptive Kalman filtering algorithm realizes the adaptive updating of variable forgetting factor by using the error between the expected output value and the actual output value,and thus realize the online updating of the prediction model.The numerical results further show that compared with the least square method and the conventional Kalman filtering algorithm,the proposed algorithm can improve the speed prediction accuracy by 87.5% and 50% respectively,implying that the proposed algorithm can provide better real-time effectiveness for the practical applications.

Key words: Speed prediction, RFID, Data acquisition, Adaptive, Kalman filter

中图分类号: 

  • TP391
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