| 基于RFID和自适应卡尔曼滤波的室内移动目标定位方法 |
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| 引用本文： | 尹姝,陈元橼,仇翔.基于RFID和自适应卡尔曼滤波的室内移动目标定位方法[J].南京气象学院学报,2018,10(6):749-753. |
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| 作者姓名： | 尹姝 陈元橼 仇翔 |
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| 作者单位： | 浙江工业大学 电工电子中心, 杭州, 310023,浙江工业大学 信息工程学院, 杭州, 310023,浙江工业大学 信息工程学院, 杭州, 310023 |
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| 基金项目： | 浙江省自然科学基金（LR16F030005）；浙江省公益项目（2016C31065） |
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| 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（Boundary-Adaptive 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UHF-RFID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.
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| 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 |
| 收稿时间： | 2018/7/19 0:00:00 |
Indoor moving-target localization using RFID and adaptive Kalman filter |
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| YIN Shu,CHEN Yuanyuan and QIU Xiang.Indoor moving-target localization using RFID and adaptive Kalman filter[J].Journal of Nanjing Institute of Meteorology,2018,10(6):749-753. |
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| Authors: | YIN Shu CHEN Yuanyuan and QIU Xiang |
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| Institution: | Center of Electrician and Electronics, Zhejiang University of Technology, Hangzhou 310023,College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023 and College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023 |
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| Abstract: | 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. |
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| Keywords: | localization ultra high frequency-radio frequency identification(UHF-RFID) boundary virtual reference adaptive Kalman filter |
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