基于区域限制的WiFi/PDR融合实时定位算法

doi:10.16180/j.cnki.issn1007-7820.2022.10.004

摘要/Abstract

摘要：

针对室内定位系统在实际应用场景中算法复杂度高、运算量较大的问题,文中设计并实现了一种基于EKF的WiFi/PDR融合定位系统。在WiFi指纹定位部分提出了基于自适应滑动窗口的指纹匹配方法,通过邻近状态的RSSI欧式距离解算得到搜索窗口,以动态调整指纹库的匹配范围,从而实现了定位结果的快速收敛。在融合定位阶段,结合EKF与PDR的系统特性来解决时间配准问题。以WiFi数据更新为基准,利用EKF算法进行数据融合,在融合数据不同步时由PDR直接输出定位结果。实验结果表明,该定位系统具有良好的运行效果与稳定性,所提方法在实际定位场景中的平均定位误差为2.27 m,并在80%的情况下能够达到3 m的定位精度。

关键词: 多源融合, 室内定位, 接收信号强度, WiFi定位, K-最近邻, PDR, 扩展卡尔曼滤波, 实时定位

Abstract:

In view of the problems of high algorithm complexity and large computation in the practical application of indoor positioning system, a WiFi/PDR integrated positioning system based on EKF is designed and implemented. In the WiFi fingerprint positioning part, a fingerprint matching method based on an adaptive sliding window is proposed. The search window is obtained by calculating the RSSI Euclidean distance in the neighboring state to dynamically adjust the matching range of the fingerprint library, thereby achieving rapid convergence of the positioning results. In the fusion localization stage, the system characteristics of EKF and PDR are combined to solve the time registration problem. Based on the WiFi data update, the EKF algorithm is used for data fusion, and the PDR directly outputs the positioning result when the fusion data is not synchronized. Experimental results show that the positioning system has good operating effects and stability. The average positioning error of the proposed method is 2.27 m in the actual positioning scene, and the positioning accuracy can reach 3 m in 80% of the cases.

Key words: multi-source fusion, indoor positioning, received signal strength, WiFi positioning, K-nearest neighbor, PDR, extended Kalman filtering, real-time positioning

中图分类号:

TN92

胡文强,胡建鹏. 基于区域限制的WiFi/PDR融合实时定位算法[J]. 电子科技, 2022, 35(10): 21-26.

HU Wenqiang,HU Jianpeng. WiFi/PDR Fusion Real-Time Localization Algorithm Based on Region Constraint[J]. Electronic Science and Technology, 2022, 35(10): 21-26.

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参考文献 18

[1]	张平, 陈昊. 面向5G的定位技术研究综述[J]. 北京邮电大学学报, 2018, 41(5):1-12. doi: 10.13190/j.jbupt.2018-185
	Zhang Ping, Chen Hao. A survey of positioning technology for 5G[J]. Journal of Beijing University of Posts and Telecommunications, 2018, 41(5):1-12. doi: 10.13190/j.jbupt.2018-185
[2]	Chen Z, Zou H, Jiang H, et al. Fusion of WiFi, smartphone sensors and landmarks using the Kalman filter for indoor localization[J]. Sensors, 2015, 15(1):715-732. doi: 10.3390/s150100715 pmid: 25569750
[3]	黄璐, 甘兴利, 李雅宁. 基于智能终端的室内定位系统研究与实现[J]. 无线电工程, 2017, 47(9):44-50.
	Huang Lu, Gan Xingli, Li Yaning. Research and implementation of indoor positioning system based on intelligent terminal[J]. Radio Engineering, 2017, 47(9):44-50.
[4]	胡安冬, 王坚, 汪云甲, 等. 利用渐消自适应EKF算法进行PDR-WiFi融合定位[J]. 武汉大学学报(信息科学版), 2016, 41(11):1556-1562.
	Hu Andong, Wang Jian, Wang Yunjia, et al. An fusion positioning for PDR and WiFi based on fading adaptive weighted EKF[J]. Geomatics and Information Science of Wuhan University, 2016, 41(11):1556-1562.
[5]	夏宇声, 刘凯, 张浩, 等. 基于多边限定的室内定位方法设计与系统实现[J]. 计算机工程与应用, 2019, 55(11):250-256. doi: 10.3778/j.issn.1002-8331.1802-0019
	Xia Yusheng, Liu Kai, Zhang Hao, et al. Multilateral-confined method for indoor localization: algorithm design and system implementation[J]. Computer Engineering and Applications, 2019, 55(11):250-256. doi: 10.3778/j.issn.1002-8331.1802-0019
[6]	刘庆, 关维国, 李顺康, 等. 基于扩展Kalman滤波的室内WiFi-PDR融合定位算法[J]. 计算机工程, 2019, 45(4):66-71.
	Liu Qing, Guan Weiguo, Li Shunkang, et al. Indoor WiFi-PDR fusion location algorithm based on extended Kalman filter[J]. Computer Engineering, 2019, 45(4):66-71.
[7]	李楠, 陈家斌, 袁燕. 基于WiFi/PDR的室内行人组合定位算法[J]. 中国惯性技术学报, 2017, 25(4):483-487.
	Li Nan, Chen Jiabin, Yuan yan. Indoor pedestrian integrated localization strategy based on WiFi/PDR[J]. Journal of Chinese Inertial Technology, 2017, 25(4):483-487.
[8]	Wang X, Qin D Y, Ma L. WiFi location fingerprint indoor positioning method based on WKNN[C]. Singapore: Proceedings of the International Conference in Communications, Signal Processing, and Systems, 2019.
[9]	朱轶峰. 基于WiFi-BP的室内定位算法[J]. 电子科技, 2020, 33(8):74-79.
	Zhu Yifeng. Indoor positioning algorithm based on WiFi-BP[J]. Electronic Science and Technology, 2020, 33(8):74-79.
[10]	Zhao H, Huang B Q, Jia B. Applying kriging interpolation for WiFi fingerprinting based indoor positioning systems[C]. Doha: Proceedings of the IEEE Wireless Communications and Networking Conference, 2016.
[11]	胡楠. 基于距离约束的WiFi指纹定位算法研究与实现[D]. 南京: 南京邮电大学, 2020.
	Hu Nan. Research and implementation of WiFi fingerprint location algorithm based on distance constraint[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2020.
[12]	Hou L J, Luo Y F, Liu Y H. Research on a fast matching method of K-nearest neighbor for WiFi fingerprint location[C]. Singapore: Proceedings of the Chinese Intelligent Systems Conference, 2018.
[13]	葛倩, 侯守明, 赵文涛. 基于卡尔曼滤波和改进DBSCAN聚类组合的GPS定位算法[J]. 全球定位系统, 2021, 46(1):28-35.
	Ge Qian, Hou Shouming, Zhao Wentao. GPS positioning algorithm based on Kalman filter and improved DBSCAN clustering combination[J]. GNSS World of China, 2021, 46(1):28-35.
[14]	洪腾腾, 胡绍林. 初值偏差对线性系统状态向量Kalman滤波的影响[J]. 自动化学报, 2017, 43(5):789-794.
	Hong Tengteng, Hu Shaolin. Effect of initial deviation on Kamlan filter of state vectors in linear systems[J]. Acta Automatica Sinica, 2017, 43(5):789-794.
[15]	孙建强, 尚俊娜, 施浒立. PDR辅助UWB的室内非视距定位方法[J]. 传感技术学报, 2020, 33(5):711-717.
	Sun Jianqiang, Shang Junna, Shi Huli. PDR-Assisted UWB indoor non-line-of-sight positioning method[J]. Chinese Journal Sensors and Actuators, 2020, 33(5):711-717.
[16]	余佩, 韩勇强. 几种常用步态检测典型算法的设计比较[J]. 导航定位与授时, 2018, 5(4):21-26.
	Yu Pei, Han Yongqiang. The comparison of several typical gait detection algorithms[J]. Navigation Positioning and Timing, 2018, 5(4):21-26.
[17]	刘春燕. 基于WiFi与惯性技术的行人室内定位算法研究[D]. 徐州: 中国矿业大学, 2015.
	Liu Chunyan. Study on the pedestrian indoor positioning algorithms based on WiFi and inertial technology[D]. Xuzhou: China University of Mining and Technology, 2015.
[18]	亢璐. 基于WiFi指纹与PDR融合的室内定位算法研究[D]. 武汉: 华中科技大学, 2019.
	Kang Lu. Research on indoor positioning based on fusion of WiFi fingerprint and PDR[D]. Wuhan: Huazhong University of Science and Technology, 2019.