基于RFID和自适应卡尔曼滤波的室内移动目标定位方法  

Indoor moving-target localization using RFID and adaptive Kalman filter

作　　者：尹姝[1] 陈元橼 仇翔[2] YIN Shu;CHEN Yuanyuan;QIU Xiang(Center of Electrician and Electronics,Zhejiang University of Technology,Hangzhou 310023;College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023)

机构地区：[1]浙江工业大学电工电子中心,杭州310023 [2]浙江工业大学信息工程学院,杭州310023

出　　处：《南京信息工程大学学报（自然科学版）》2018年第6期749-753,791,共6页Journal of Nanjing University of Information Science & Technology（Natural Science Edition）

基　　金：浙江省自然科学基金(LR16F030005);浙江省公益项目(2016C31065)

摘　　要：本文研究了UHF-RFID(超高频-射频识别)环境下的移动目标定位问题,提出了一种结合自适应卡尔曼滤波和BVIRE(边界虚拟参考标签)的移动机器人定位方法,即B-AKF (BoundaryAdaptive Kalman Filter)定位方法.首先,利用UHF-RFID系统对移动机器人进行初始定位,其次,考虑模型和噪声统计特性不确定性,采用自适应卡尔曼滤波方法对机器人的运动状态进行预测和更新,并引入自适应因子补偿噪声方差不确定性问题.最后,搭建了基于UHFRFID的定位实验平台,并通过实验研究表明,相比于传统的线性BVIRE和线性卡尔曼滤波方法,所提出的自适应卡尔曼滤波方法具有更高的定位精度和更强的鲁棒性能.The UHF-RFID-based moving-target localization problem is investigated in this paper.In addition,a novel localization method,named B-AFK method,which integratesthe adaptive Kalman filter and the conventional BVIRE method,is presented.First,the UHF-RFID system is used to initialize the location of a mobile robot.Then,considering the uncertainties in the model and the statistical properties of the UHF-RFID localization system,an adaptive Kalman filter method is used to predict and update the robot s position.Further,an adaptive factor is introduced to compensate for the uncertainties in the noise covariance.Finally,a UHF-RFID-based localization platform is established and experiments are carried out to show that the proposed method outperforms the conventional BVIRE and linear Kalman filter in terms of precision and robustness.

关 键 词：定位 超高频-射频识别 边界虚拟参考标签 自适应卡尔曼滤波器 

分 类 号：TP249[自动化与计算机技术—检测技术与自动化装置]