Indoor EKF localization algorithm based on RFID carrier phase

Liangbo XIE; Xixi LIU; Yong WANG; Mu ZHOU; Zengshan TIAN

doi:10.11959/j.issn.1000-436x.2022048

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Indoor EKF localization algorithm based on RFID carrier phase

Papers | 更新时间：2024-06-05

- Indoor EKF localization algorithm based on RFID carrier phase
- Journal on Communications Vol. 43, Issue 3, Pages: 124-134(2022)
- 作者机构：
  
  重庆邮电大学通信与信息工程学院，重庆 400065
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61704015);The National Natural Science Foundation of China(61771083);The Natural Science Foundation of Chongqing(cstc2019jcyj-msxmX0108);Chongqing Natural Science Foundation Project(cstc2020jcyj-msxmX0842)
- DOI：10.11959/j.issn.1000-436x.2022048
  CLC： TN92
- Online First：2022-03，
  
  Published：25 March 2022
- 稿件说明：
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Liangbo XIE, Xixi LIU, Yong WANG, et al. Indoor EKF localization algorithm based on RFID carrier phase[J]. Journal on Communications, 2022, 43(3): 124-134.
DOI：

Liangbo XIE, Xixi LIU, Yong WANG, et al. Indoor EKF localization algorithm based on RFID carrier phase[J]. Journal on Communications, 2022, 43(3): 124-134. DOI： 10.11959/j.issn.1000-436x.2022048.

摘要

为解决现有超高频射频识别定位方法受室内环境干扰导致定位精度不高的问题，提出了一种基于跳频辅助的 RFID 载波相位室内扩展卡尔曼滤波（EKF）定位算法。利用跳频获取的虚拟大带宽进行距离粗估计以实现多径抑制，并通过多径抑制后的相位完成可靠双频点选择以及参数优化，最终采用 EKF 算法实现高精度快速定位。实验结果表明，所提算法平均定位误差为9.35 cm，定位解算实时性比传统的基于中国剩余定理（CRT）的解整周方法提高了近10倍。

Abstract

In order to solve the problem of low localization accuracy caused by indoor environment interference in the existing UHF RFID localization methods

an indoor extended Kalman filter (EKF) localization algorithm based on frequency hopping assisted RFID carrier phase was proposed.The virtual large bandwidth obtained by frequency hopping was used for rough distance estimation to realize multipath suppression

and the reliable dual frequency point selection and parameter optimization were completed through the phase after multipath suppression.Finally

EKF algorithm was used to realize high-precision and fast localization.Experimental results show that the average localization error of the proposed algorithm is 9.35 cm

and the real-time performance of the localization solution is nearly 10 times higher than the traditional integer solution method based on Chinese remainder theorem (CRT).

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references

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