基于频率响应的FTTR WLAN室内无线定位算法研究

doi:10.11959/j.issn.2096-3750.2023.00355

物联网学报 ›› 2023, Vol. 7 ›› Issue (3): 72-84.doi: 10.11959/j.issn.2096-3750.2023.00355

• 专题：短距无线通信技术 • 上一篇

基于频率响应的FTTR WLAN室内无线定位算法研究

龙智丰¹, 张靖¹^,²

¹ 华中科技大学电子信息与通信学院，湖北武汉 430074
² 华中科技大学绿色通信与网络国际联合研究中心，湖北武汉 430074

修回日期:2023-05-29 出版日期:2023-09-01 发布日期:2023-09-01
作者简介:龙智丰（2000- ），男，华中科技大学电子信息与通信学院硕士生，主要研究方向为无线通信、室内定位
张靖（1975- ），男，博士，华中科技大学电子信息与通信学院副教授，主要研究方向为无线通信、绿色通信、短距接入网络、光网络和无线网络融合、下一代通信网络等
基金资助:
国家自然科学基金资助项目(U2001210);国家重点研发计划(2020YFB1806605);湖北省重点研发计划(2021BAA009)

Research on FTTR WLAN indoor wireless location algorithm based on frequency response

Zhifeng LONG¹, Jing ZHANG¹^,²

¹ School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
² International Joint Research Center of Green Communications and Networking, Huazhong University of Science and Technology, Wuhan 430074, China

Revised:2023-05-29 Online:2023-09-01 Published:2023-09-01
Supported by:
The National Natural Science Foundation of China(U2001210);The National Key Research and Development Program of China(2020YFB1806605);The Key Research and Development Program of Hubei Province(2021BAA009)

摘要/Abstract

摘要：

高精度和可靠的室内无线定位服务已经被广泛使用，为了获得良好的定位精度，定位算法的设计需要与无线定位设施相匹配。全屋光纤（FTTR, fiber to the room）是基于新一代无线局域网（WLAN, wireless local area network）标准IEEE 802.11 ax所开发的室内接入网络方案。相较于已有的Wi-Fi网络，FTTR可用频带宽度大大增加，同时FTTR WLAN也缺乏支持定位功能的公共有效数据集，这使得基于FTTR WALN场景的定位研究面临巨大障碍。为了解决上述问题，首先，提出基于频率响应的FTTR WLAN场景数据集生成方法，利用已有的Wi-Fi定位数据集生成FTTR可用频带宽度内的频率响应矩阵；然后，提出利用并行路径的主成分分析（PCA, principal component analysis）的方法生成分类矩阵，并利用全连接神经网络对生成的数据集进行训练来提高精度。在真实测量数据集上的实验结果表明，所提定位算法可以达到误差小于1 m的定位精度，不仅比传统位置估计算法精度更高，而且基本达到了实际应用的细粒度定位要求。

关键词: 全屋光纤, 数据集合成, 主成分分析

Abstract:

Highly accurate and reliable indoor wireless positioning services have been widely used.In order to obtain good positioning accuracy, the design of positioning algorithms needs to be matched with wireless positioning facilities.fiber to the room (FTTR) is an indoor access network solution based on IEEE 802.11 ax, a new generation of wireless local area network (WLAN) standard.Compared with the existing Wi-Fi networks, FTTR has a much larger available band width.However, FTTR WLAN also lacks of a public valid data set to support localization functions, which makes the localization research based on FTTR scenarios face huge obstacles.In order to solve the above problems, firstly, a frequency response-based FTTR scene dataset generation method was proposed, which uses the existing Wi-Fi localization dataset to generate the frequency response matrix within the available band width of FTTR.Then, the parallel path principal component analysis (PCA) method was used to generate the classification matrix.And the generated dataset was trained using a fully connected neural network to improve the accuracy.The experimental results on the real measurement dataset show that the proposed localization algorithm can achieve a localization accuracy of less than 1 m, which is not only more accurate than the traditional location estimation algorithm, but also basically meets the fine-grained localization requirements for practical applications.

Key words: FTTR, dataset synthesis, principal component analysis

中图分类号:

TN929.5

龙智丰, 张靖. 基于频率响应的FTTR WLAN室内无线定位算法研究[J]. 物联网学报, 2023, 7(3): 72-84.

Zhifeng LONG, Jing ZHANG. Research on FTTR WLAN indoor wireless location algorithm based on frequency response[J]. Chinese Journal on Internet of Things, 2023, 7(3): 72-84.

图/表 10

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

参考文献 31

[1]	ZHU X Q , QU W Y , QIU T ,et al. Indoor intelligent fingerprint-based localization:principles,approaches and challenges[J]. IEEE Communications Surveys ＆ Tutorials, 2020,22(4): 2634-2657.
[2]	ZAFARI F , GKELIAS A , LEUNG K K . A survey of indoor localization systems and technologies[J]. IEEE Communications Surveys ＆Tutorials, 2019,21(3): 2568-2599.
[3]	HE C , LIU Y Q , HUANG Y D ,et al. Q-band millimeter wave communication:enabling 10 gb/s home network in fiber-to- the-room scenario[C]// Proceedings of 2021 IEEE MTT-S International Wireless Symposium (IWS). Piscataway:IEEE Press, 2021: 1-3.
[4]	ZHANG Y , QU C , WANG Y J . An indoor positioning method based on CSI by using features optimization mechanism with LSTM[J]. IEEE Sensors Journal, 2020,20(9): 4868-4878.
[5]	ZHANG Y , WU C B , CHEN Y . A low-overhead indoor positioning system using CSI fingerprint based on transfer learning[J]. IEEE Sensors Journal, 2021,21(16): 18156-18165.
[6]	G?NüLTA? E , LEI E , LANGERMAN J ,et al. CSI-based multi-antenna and multi-point indoor positioning using probability fusion[J]. IEEE Transactions on Wireless Communications, 2022,21(4): 2162-2176.
[7]	PINTO B , BARRETO R , SOUTO E ,et al. Robust RSSI-based indoor positioning system using K-means clustering and Bayesian estimation[J]. IEEE Sensors Journal, 2021,21(21): 24462-24470.
[8]	SONG Q W , GUO S T , LIU X ,et al. CSI amplitude fingerprinting-based NB-IoT indoor localization[J]. IEEE Internet of Things Journal, 2018,5(3): 1494-1504.
[9]	WANG X Y , GAO L J , MAO S W ,et al. CSI-based fingerprinting for indoor localization:a deep learning approach[J]. IEEE Transactions on Vehicular Technology, 2017,66(1): 763-776.
[10]	WANG C , LUO J , LIU X ,et al. Secure and reliable indoor localization based on multitask collaborative learning for large-scale buildings[J]. IEEE Internet of Things Journal, 2022,9(22): 22291-22303.
[11]	TAO Y , ZHAO L . AIPS:an accurate indoor positioning system with fingerprint map adaptation[J]. IEEE Internet of Things Journal, 2022,9(4): 3062-3073.
[12]	KIM H , GRANSTR?M K , GAO L ,et al. 5G mmWave cooperative positioning and mapping using multi-model PHD filter and map fusion[J]. IEEE Transactions on Wireless Communications, 2020,19(6): 3782-3795.
[13]	BUTT M M , RAO A , YOON D . RF fingerprinting and deep learning assisted UE positioning in 5G[C]// Proceedings of 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring). Piscataway:IEEE Press, 2020: 1-7.
[14]	CHEN C , CHEN Y , HAN Y ,et al. Achieving centimeter-accuracy indoor localization on Wi-Fi platforms:a multi-antenna approach[J]. IEEE Internet of Things Journal, 2017,4(1): 122-134.
[15]	MAGSINO E R , HO I W H , SITU Z H . The effects of dynamic environment on channel frequency response-based indoor positioning[C]// Proceedings of 2017 IEEE 28th Annual International Symposium on Personal,Indoor,and Mobile Radio Communications (PIMRC). Piscataway:IEEE Press, 2018: 1-6.
[16]	TSENG P H , CHAN Y C , LIN Y J ,et al. Ray-tracing-assisted fingerprinting based on channel impulse response measurement for indoor positioning[J]. IEEE Transactions on Instrumentation and Measurement, 2017,66(5): 1032-1045.
[17]	JEON N R , LEE C H , KANG N G ,et al. Performance of channel prediction using 3D ray-tracing scheme compared to conventional 2D scheme[C]// Proceedings of 2006 Asia-Pacific Conference on Communications. Piscataway:IEEE Press, 2006: 1-6.
[18]	NAGARAJ S , KHAN S , SCHLEGEL C ,et al. Differential preamble detection in packet-based wireless networks[J]. IEEE Transactions on Wireless Communications, 2009,8(2): 599-607.
[19]	YAN J , QI G W , KANG B ,et al. Extreme learning machine for accurate indoor localization using RSSI fingerprints in multi floor environments[J]. IEEE Internet of Things Journal, 2021,8(19): 14623-14637.
[20]	RAMBHATLA S , LI X G , REN J N ,et al. A dictionary-based generalization of robust PCA with applications to target localization in hyper spectral imaging[J]. IEEE Transactions on Signal Processing, 2020,(68): 1760-1775.
[21]	NJIMA W , BAZZI A , CHAFII M . DNN-based indoor localization under limited dataset using GANs and semi-supervised learning[J]. IEEE Access, 2022(10): 69896-69909.
[22]	CHEN C Y , LAI A I C , WU P Y ,et al. Optimization and evaluation of multi detector deep neural network for high-accuracy Wi-Fi fingerprint positioning[J]. IEEE Internet of Things Journal, 2022,9(16): 15204-15214.
[23]	SHI J , WANG G , JIN L P . Least squared relative error estimator for RSS based localization with unknown transmit power[J]. IEEE Signal Processing Letters, 2020(27): 1165-1169.
[24]	WATANABE F . Wireless sensor network localization using AoA measurements with two-step error variance-weighted least squares[J]. IEEE Access, 2021(9): 10820-10828.
[25]	YU X F , HU X , LIU Z J ,et al. A method to select optimal deep neural network model for power amplifiers[J]. IEEE Microwave and Wireless Components Letters, 2021,31(2): 145-148.
[26]	TóTH Z , TAMáS J . Miskolc IIS hybrid IPS:dataset for hybrid indoor positioning[C]// Proceedings of 2016 26th International Conference Radioelektronika (RADIOELEKTRONIKA). Piscataway:IEEE Press, 2016: 408-412.
[27]	WEI W , YAN J , WU X F ,et al. CSI fingerprinting for device-free localization:phase calibration and SSIM-based augmentation[J]. IEEE Wireless Communications Letters, 2022,11(6): 1137-1141.
[28]	LI S , LEI W M , ZHANG W ,et al. Weighted TSVR based nonlinear channel frequency response estimation for MIMO-OFDM system[J]. IEEE Access, 2020(8): 224283-224291.
[29]	THENUARDI D S , SOEWITO B . Indoor positioning system using WKNN and LSTM combined via ensemble learning[J]. Advances in Science,Technology and Engineering Systems Journal, 2020,6(1): 242-249.
[30]	HE K M , ZHANG X Y , REN S Q ,et al. Deep residual learning for image recognition[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2016: 770-778.
[31]	CHOLLET F . Xception:deep learning with depth wise separable convolutions[C]// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2017: 1800-1807.

基于频率响应的FTTR WLAN室内无线定位算法研究

Research on FTTR WLAN indoor wireless location algorithm based on frequency response

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 31

相关文章 1

Metrics

推荐阅读 0