CSI Indoor Location Method Based on Stacked Sparse Auto-encoder and SVM

ZHANG Huiqing; WANG Yutong

doi:10.11936/bjutxb2020110017

Journal of Beijing University of Technology > 2021 > 47(12): 1321-1329. > DOI: 10.11936/bjutxb2020110017

ZHANG Huiqing, WANG Yutong. CSI Indoor Location Method Based on Stacked Sparse Auto-encoder and SVM[J]. Journal of Beijing University of Technology, 2021, 47(12): 1321-1329. DOI: 10.11936/bjutxb2020110017

Citation:

PDF (3671 KB)

CSI Indoor Location Method Based on Stacked Sparse Auto-encoder and SVM

ZHANG Huiqing^{1, 2, 3,},
WANG Yutong^{1, 2, 3}

1.
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2.
Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China
3.
Beijing Laboratory for Urban Mass Transit, Beijing 100124, China

More Information

Received Date: November 12, 2020
Available Online: August 03, 2022
Published Date: December 09, 2021

Graphical Abstract

Abstract

Abstract

In response to the problem of data redundancy and resolve complex of the indoor location based on high fine-grained channel state information (CSI), when using CSI as fingerprint, a CSI indoor location method based on stacked sparse auto-encoder and support vector machine (SVM) was proposed. First, the amplitude and phase data of physical layer channel information were combined, and the stacked sparse auto-encoder was used to extract the deep location features in the nonlinear fingerprint feature space. Then, sparse feature fingerprint was generated and target location was determined by support vector classifier. The application of sparse feature fingerprint reduces the size of CSI fingerprint database by about 92.6%. Meanwhile, experimental results show that the proposed method can achieve an average positioning error of 1.205 m in a complex indoor environment with mixed line-of-sight and non-line-of-sight propagation paths, and the positioning accuracy is significantly improved compared with other methods.
- indoor positioning,
- channel state information (CSI),
- feature extraction,
- sparse feature fingerprint,
- auto-encoder,
- support vector machine (SVM)

FullText(HTML)

References (23)

References

[1]	HE S, CHAN S H G. Wi-Fi fingerprint-based indoor positioning: recent advances and comparisons[J]. IEEE Communications Surveys & Tutorials, 2015, 18(1): 466-490. http://www.cs.ust.hk/faculty/gchan/papers/COMST16_IP.pdf
[2]	陈锐志, 叶锋. 基于Wi-Fi信道状态信息的室内定位技术现状综述[J]. 武汉大学学报(信息科学版), 2018, 43(12): 2064-2070. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201812034.htm CHEN R Z, YE F. An overview of indoor positioning technology based on Wi-Fi channel state information[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 2064-2070. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201812034.htm
[3]	YANG X. IEEE 802. 11 n: enhancements for higher throughput in wireless LANs[J]. IEEE Wireless Communications, 2005, 12(6): 82-91. doi: 10.1109/MWC.2005.1561948
[4]	VAN NEE R, JONES V K, AWATER G, et al. The 802. 11 n MIMO-OFDM standard for wireless LAN and beyond[J]. Wireless Personal Communications, 2006, 37(3/4): 445-453. http://www.mendeley.com/research/greg-steele-802-11-n-mimo-ofdm-standard-wireless-lan-beyond/
[5]	杨睿哲, 毕瑞琪, 司鹏搏, 等. 基于统计特性的多天线能效优化[J]. 北京工业大学学报, 2017, 43(6): 853-858. doi: 10.11936/bjutxb2016070022 YANG R Z, BI R Q, SI P B, et al. Energy efficiency optimization for multi-antenna systems based on statistical characteristics[J]. Journal of Beijing University of Technology, 2017, 43(6): 853-858. (in Chinese) doi: 10.11936/bjutxb2016070022
[6]	YANG Z, ZHOU Z, LIU Y. From RSSI to CSI: indoor localization via channel response[J]. ACM Computing Surveys (CSUR), 2013, 46(2): 1-32. http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=93619888&site=ehost-live
[7]	SHI S Y, SIGG S, CHEN L, et al. Accurate location tracking from CSI-based passive device-free probabilistic fingerprinting[J]. IEEE Transactions on Vehicular Technology, 2018, 67(6): 5217-5230. doi: 10.1109/TVT.2018.2810307
[8]	GABER A, OMAR A. A study of wireless indoor positioning based on joint TDOA and DOA estimation using 2-D matrix pencil algorithms and IEEE 802. 11 ac[J]. IEEE Transactions on Wireless Communications, 2014, 14(5): 2440-2454. http://www.onacademic.com/detail/journal_1000037360398710_a935.html
[9]	KOTARU M, JOSHI K, BHARADIA D, et al. Spotfi: decimeter level localization using WIFI[C]//Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication. New York: ACM, 2015: 269-282.
[10]	XIAO J, WU K, YI Y, et al. FIFS: fine-grained indoor fingerprinting system[C]//201221st International Conference on Computer Communications and Networks (ICCCN). Piscataway: IEEE, 2012: 1-7.
[11]	CHAPRE Y, IGNJATOVIC A, SENEVIRATNE A, et al. Csi-mimo: indoor Wi-Fi fingerprinting system[C]//39th Annual IEEE Conference on Local Computer Networks. Piscataway: IEEE, 2014: 202-209.
[12]	WANG X, GAO L, MAO S, et al. CSI-based fingerprinting for indoor localization: a deep learning approach[J]. IEEE Transactions on Vehicular Technology, 2017, 66(1): 763-776. http://www.researchgate.net/profile/Xuyu_Wang2/publication/299285077_CSI-based_Fingerprinting_for_Indoor_Localization_A_Deep_Learning_Approach/links/56f053c008ae70bdd6c948f9.pdf
[13]	WANG X, GAO L, MAO S. CSI phase fingerprinting for indoor localization with a deep learning approach[J]. IEEE Internet of Things Journal, 2016, 3(6): 1113-1123. doi: 10.1109/JIOT.2016.2558659
[14]	VALENTINE A P, TRAMPERT J. Data space reduction, quality assessment and searching of seismograms: autoencoder networks for waveform data[J]. Geophysical Journal International, 2012, 189(2): 1183-1202. doi: 10.1111/j.1365-246X.2012.05429.x
[15]	DENG W, YAO R, ZHAO H, et al. A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm[J]. Soft Computing, 2019, 23(7): 2445-2462. doi: 10.1007/s00500-017-2940-9
[16]	徐晓苏, 吴晓飞, 张涛, 等. 基于SALDE-UKF-SVM算法的WLAN室内定位方法[J]. 中国惯性技术学报, 2017, 25(6): 731-737. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGXJ201706006.htm XU X S, WU X F, ZHANG T, et al. WLAN indoor positioning based on SALDE-SVM algorithm[J]. Journal of Chinese Inertial Technology, 2017, 25(6): 731-737. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGXJ201706006.htm
[17]	HALPERIN D, HU W, SHETH A, et al. Tool release: gathering 802. 11 n traces with channel state information[J]. ACM SIGCOMM Computer Communication Review, 2011, 41(1): 53. doi: 10.1145/1925861.1925870
[18]	PARK C H, CHANG J H. WLS Localization using skipped filter, hampel filter, bootstrapping and Gaussian mixture EM in LOS/NLOS conditions[J]. IEEE Access, 2019, 7: 35919-35928. doi: 10.1109/ACCESS.2019.2905367
[19]	ZHUO Y, ZHU H, XUE H. Identifying a new non-linear CSI phase measurement error with commodity WiFi devices[C]//2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS). Piscataway: IEEE, 2016: 72-79.
[20]	QIAN K, WU C, YANG Z, et al. PADS: passive detection of moving targets with dynamic speed using PHY layer information[C]//2014 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS). Piscataway: IEEE, 2014: 1-8.
[21]	ZHOU R, CHEN J, LU X, et al. CSI fingerprinting with SVM regression to achieve device-free passive localization[C]//2017 IEEE 18th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM). Piscataway: IEEE, 2017: 1-9.
[22]	XU J, XIANG L, LIU Q, et al. Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images[J]. IEEE Transactions on Medical Imaging, 2015, 35(1): 119-130. http://www.researchgate.net/profile/Jun_Xu31/publication/280327332_Stacked_Sparse_Autoencoder_SSAE_for_Nuclei_Detection_on_Breast_Cancer_Histopathology_Images/links/56b92e4208ae39ea9905c852.pdf
[23]	SUN J, YAN C, WEN J. Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning[J]. IEEE Transactions on Instrumentation and Measurement, 2017, 67(1): 185-195. http://ieeexplore.ieee.org/document/8097416/

Cited By

Cited by

Periodical cited type(3)

1.	杜月，王慧琴，余兆媛，钱亚林，魏敏俊. 基于堆叠稀疏自编码器与GAN的线损分层定位. 电子设计工程. 2025(05): 115-119 .
2.	程振豪，李林阳，郭文卓，赖路广，赵冬青. 5G信道状态信息信号质量及指纹定位性能分析. 全球定位系统. 2024(02): 16-22 .
3.	杨娟，滕飞，郭大林. 多模态融合的特征提取方法在SA检测中的应用. 计算机与现代化. 2022(10): 121-126 .

Other cited types(3)

Get Citation

PDF

XML

Article views (163) PDF downloads (70) Cited by(6)

Turn off MathJax

Article Contents

Abstract

References

CSI Indoor Location Method Based on Stacked Sparse Auto-encoder and SVM

Abstract

References

Cited by

Periodical cited type(3)

Other cited types(3)

Catalog

Export File

Citation

Format

Content