基于CSI相位采样密度的室内定位技术研究

程诚, 王伟

集成电路与嵌入式系统 ›› 2024, Vol. 24 ›› Issue (1) : 73-77.

PDF(1149 KB)
PDF(1149 KB)
集成电路与嵌入式系统 ›› 2024, Vol. 24 ›› Issue (1) : 73-77. DOI: 10.20193/j.ices2097-4191.2024.01.011
研究论文

基于CSI相位采样密度的室内定位技术研究

作者信息 +

Indoor positioning technology based on CSI phase sampling density

Author information +
文章历史 +

摘要

基于信道状态信息(CSI)的室内定位技术已经被广泛应用于各种场合,在CSI相位指纹定位方案中,采样点密度大小与定位效果好坏紧密相关。由于数据采集工作量的关系,以往学者们多关注低采样密度场景或通过模拟仿真分析采样密度问题,导致难以找到定位误差最小的采样密度。本文设置不同的采样密度,将数据预处理后的CSI相位信息作为指纹,匹配WKNN算法,分析探究了不同采样点密度对于定位精度的影响。实验结果表明,在4 m×4 m的环境中,采样间隔设置为0.4 m,采样密度为7.6个/m2时,定位精度最高,平均误差为0.34 m,同时兼顾采样工作量,具有较高性价比。

Abstract

Indoor location technology based on channel state information (CSI) has been widely used in various places,in the CSI phase fingerprint location scheme,the density of sampling points is closely related to the location effect.Due to the workload of data collection,previous scholars paid more attention to low-sampling density scenarios or adopted simulation methods to analyze sampling density research,which made it difficult to find the sampling density with the smallest positioning error.In this paper,the author first sets different CSI sampling densities,takes the CSI phase after data preprocessing as fingerprint characteristics,and then uses WKNN algorithm to analyze and explore the influence of different sampling densities on the positioning effect.The experiment results show that in the environment of 4 m×4 m,when a sampling point is taken every 0.4 m and the sampling density is 7.6 per square meter,the positioning accuracy is the highest,the average error is 0.34 m,and the sampling workload is taken into account.

关键词

信道状态信息 / 指纹定位 / 室内定位技术 / 采样密度 / WKNN算法

Key words

channel state information / fingerprint location / indoor location technology / sampling density / WKNN algorithm

引用本文

导出引用
程诚, 王伟. 基于CSI相位采样密度的室内定位技术研究[J]. 集成电路与嵌入式系统. 2024, 24(1): 73-77 https://doi.org/10.20193/j.ices2097-4191.2024.01.011
CHENG Cheng, WANG Wei. Indoor positioning technology based on CSI phase sampling density[J]. Integrated Circuits and Embedded Systems. 2024, 24(1): 73-77 https://doi.org/10.20193/j.ices2097-4191.2024.01.011
中图分类号: TN919   

参考文献

[1]
施闯, 赵齐乐, 李敏. 北斗卫星导航系统的精密定轨与定位研究[J]. 中国科学:地球科学, 2012(6):64-71.
SHI CH, ZHAO Q L, LI M. Research on Precision Orbiting and Positioning of Beidou Satellite Navigation System[J]. Chinese Science:Earth Science, 2012(6):64-71 (in Chinese).
[2]
MC NEFF, J G. The global positioning system[J]. IEEE Transactions on Microwave Theory and Techniques, 2002, 50(3):645-652.
[3]
GUO K, QIU Z, MIAO C, et al. Ultra-Wideband-Based Localization for Quadcopter NaviGation[J]. unmanned systems, 2016, 4(1):23-34.
Micro unmanned aerial vehicles (UAVs) are promising to play more and more important roles in both civilian and military activities. Currently, the navigation of UAVs is critically dependent on the localization service provided by the Global Positioning System (GPS), which suffers from the multipath effect and blockage of line-of-sight, and fails to work in an indoor, forest or urban environment. In this paper, we establish a localization system for quadcopters based on ultra-wideband (UWB) range measurements. To achieve the localization, a UWB module is installed on the quadcopter to actively send ranging requests to some fixed UWB modules at known positions (anchors). Once a distance is obtained, it is calibrated first and then goes through outlier detection before being fed to a localization algorithm. The localization algorithm is initialized by trilateration and sustained by the extended Kalman filter (EKF). The position and velocity estimates produced by the algorithm will be further fed to the control loop to aid the navigation of the quadcopter. Various flight tests in different environments have been conducted to validate the performance of UWB ranging and localization algorithm.
[4]
HUANG C H, LEE L H, HO C C, et al. Real-time RFID indoor positioning system based on Kalman-filter drift removal and Heron-bilateration Location estimation[J]. IEEE Transactions on Instrumentation and Measurement, 2015, 64(3):728-739.
[5]
GUO X, LI L, ANSARI N, et al. Accurate WiFi Localization by Fusing a Group of Fingerprints Via Global Fusion Profile[J]. IEEE Transactions on Vehicular Technology, 2018:1.
[6]
王博远, 贾瑞才, 贾浩男. 高精度位置服务终端技术与应用[J]. 无线电工程, 2016, 46(11):5-8.
WANG B Y, JIA R C, JIA H N. High precision location service terminal technology and application[J]. Radio Engineering, 2016, 46(11):5-8 (in Chinese).
[7]
HALPERIN D, HU W, SHETH A, et al. Predictable 802.11 packet delivery from wireless channel measurements[J]. ACM Sigeomm Computer Communication Review, 2010, 40(4):159-170.
[8]
XIONG J, JAMIESON K. Array Track: A Fine-Grained Indoor Location System[C]// NSDI. 2013:71-84.
[9]
刘颜星, 郝占军, 田冉. 基于CSI信号的被动式室内指纹定位算法研究[J]. 计算机工程与科学, 2021, 43(8):1398-1404.
LIU Y X, HAO ZH J, TIAN R. Research on passive indoor fingerprint localization algorithm based on CSI signal[J]. Computer Engineering and Science, 2021, 43(8):1398-1404 (in Chinese).
[10]
王震. 基于电力线和Wi-Fi的通信定位系统研究[D]. 苏州: 苏州大学, 2018.
WANG ZH. Research on Communication Positioning System Based on Power Line and Wi Fi[D]. Suzhou: Suzhou University, 2018 (in Chinese).
[11]
杨帆. 基于通用移动终端多源融合技术的室内定位系统设计与实现[D]. 成都: 电子科技大学, 2021.
YANG F. Design and Implementation of Indoor Positioning System Based on Universal Mobile Terminal Multi source Fusion Technology[D]. Chengdu: University of Electronic Science and Technology, 2021 (in Chinese).
[12]
邓中亮, 尹露, 唐诗浩, 等. 室内定位关键技术综述[J]. 导航定位与授时, 2018, 5(3):14-23.
DENG ZH L, YIN L, TANG SH H, et al. Overview of Key Technologies for Indoor Positioning[J]. Navigation Positioning and Timing, 2018, 5(3):14-23 (in Chinese).
[13]
孙沙沙, 梁泉泉. 基于CSI的室内指纹定位研究[J]. 通信技术, 2019, 52(2):318-322.
SUN SH SH, LIANG Q Q. Research on indoor fingerprint localization based on CSI[J]. Communication Technology, 2019, 52(2):318-322 (in Chinese).

责任编辑: 薛士然
PDF(1149 KB)

Accesses

Citation

Detail

段落导航
相关文章

/