Indoor Wi-Fi/PDR Fusion Localization Based on Adaptive and Robust Kalman Filter

ZHOU Mu; GENG Xiao-long; XIE Liang-bo; NIE Wei; TIAN Zeng-shan

doi:10.3969/j.issn.0372-2112.2019.01.002

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Indoor Wi-Fi/PDR Fusion Localization Based on Adaptive and Robust Kalman Filter

更新时间：2025-07-16

- Indoor Wi-Fi/PDR Fusion Localization Based on Adaptive and Robust Kalman Filter
- Acta Electronica Sinica Vol. 47, Issue 1, Pages: 9-15(2019)
- 作者机构：
  
  重庆邮电大学移动通信技术重庆市重点实验室,重庆,400065
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2019.01.002
  CLC： TN961
- Published Online：25 January 2019，
  
  Published：2019
- 稿件说明：
移动端阅览
ZHOU Mu, GENG Xiao-long, XIE Liang-bo, et al. Indoor Wi-Fi/PDR Fusion Localization Based on Adaptive and Robust Kalman Filter[J]. Acta Electronica Sinica, 2019, 47(1): 9-15.
DOI：

ZHOU Mu, GENG Xiao-long, XIE Liang-bo, et al. Indoor Wi-Fi/PDR Fusion Localization Based on Adaptive and Robust Kalman Filter[J]. Acta Electronica Sinica, 2019, 47(1): 9-15. DOI： 10.3969/j.issn.0372-2112.2019.01.002.

摘要

针对室内复杂环境下信道状态信息的动态性问题，本文提出了一种面向室内Wi-Fi/行人航迹推算（Pedestrian Dead Reckoning，PDR）融合定位的自适应鲁棒卡尔曼滤波方法.该方法利用自适应鲁棒卡尔曼滤波将Wi-Fi传播模型与PDR定位信息进行多重融合，推算用户的最优估计位置.同时，基于滤波反馈机制，通过融合定位结果对加权最小二乘法中的路径损耗指数和滤波模型中的观测协方差进行动态修正，保证Wi-Fi传播模型接近于真实室内环境.实验结果表明，该方法能够有效解决室内复杂环境下单一Wi-Fi定位精度低和PDR累积误差的问题，此外，路径损耗指数和观测协方差的实时修正可以提高融合定位系统的定位精度和稳定性.

Abstract

In response to the problem of dynamic channel state information in complex indoor environment

this paper proposes an adaptive and robust Kalman filter approach for indoor Wi-Fi/Pedestrian Dead Reckoning (PDR) fusion localization.This approach conducts the multiple location information fusion of Wi-Fi propagation model and PDR to infer the optimal location estimate of the user.At the same time

based on the filter feedback mechanism

the fusion localization result is used to dynamically modify the path loss exponent in weighted least square method as well as the observation covariance in filter model with the purpose of guaranteeing that the Wi-Fi propagation model is close to the real indoor environment.The experimental results indicate that the proposed method is capable of well solving the problems of low localization accuracy by using the Wi-Fi solely and accumulative error in PDR.Furthermore

the real-time modification of path loss exponent and observation covariance improves the stability of the proposed fusion localization system.

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