WiHlo: A Case Study of WiFi-Based Human Passive Localization by Angle Refinement

  • Zengshan Tian
  • Weiqin YangEmail author
  • Yue Jin
  • Gongzhui Zhang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 313)


The emergence of the Internet of Things (IoT) has promoted the interconnection of all things. And the access control of devices and accurate service promotion are inseparable from the acquisition of location information. We propose WiHlo, a passive localization system based on WiFi Channel State Information (CSI). WiHlo directly estimates the human location by refining the angle-of-arrival (AoA) of the subtle human reflection. WiHlo divides the received signals into static path components and dynamic path components, and uses phase offsets compensation and direct wave suppression algorithms to separate out the dynamic path signals. By combining the measured AoAs and time-of-arrivals (ToAs) with Gaussian mean clustering and probability analysis, WiHlo identifies the human reflection path from the dynamic paths. Our implementation and evaluation on commodity WiFi devices demonstrate WiHlo outperforms the state-of-the-art AoA estimation system in actual indoor environment.


WiFi Passive localization AoA 


  1. 1.
    Bahl, P., Padmanabhan, V.N.: RADAR: an in-building RF-based user location and tracking system. In: INFOCOM Nineteenth Joint Conference of the IEEE Computer & Communications Societies. IEEE (2000)Google Scholar
  2. 2.
    Xiong, J., Jamieson, K.: ArrayTrack: a fine-grained indoor location system. In: USENIX Conference on Networked Systems Design & Implementation (2013)Google Scholar
  3. 3.
    Kotaru, M., Joshi, K., Bharadia, D., Katti, S.: SpotFi: decimeter level localization using WiFi. ACM SIGCOMM Comput. Commun. Rev. 45(4), 269–282 (2015) CrossRefGoogle Scholar
  4. 4.
    Mao, W., He, J., Qiu, L.: CAT: high-precision acoustic motion tracking. In: International Conference on Mobile Computing & Networking (2016)Google Scholar
  5. 5.
    Adib, F.M., Kabelac, Z., Katabi, D., Miller, R.C.: 3D tracking via body radio reflections (2014)Google Scholar
  6. 6.
    Xu, C., Gao, M., Firner, B., Zhang, Y., Li, J.: Towards robust device-free passive localization through automatic camera-assisted recalibration. In: SenSys (2012)Google Scholar
  7. 7.
    Schmidt, R.: Multiple emitter location and signal parameter estimation. IEEE Trans. Antennas Propag. 34(3), 276–280 (1986)CrossRefGoogle Scholar
  8. 8.
    Li, X., Li, S., Zhang, D., Xiong, J., Wang, Y., Mei, H.: Dynamic-MUSIC: accurate device-free indoor localization. In: ACM International Joint Conference on Pervasive & Ubiquitous Computing (2016)Google Scholar
  9. 9.
    Güngör, E., Özmen, A.: Distance and density based clustering algorithm using Gaussian kernel. Expert Syst. Appl. 69, 10–20 (2017)CrossRefGoogle Scholar
  10. 10.
    Xie, Y., Li, Z., Li, M.: Precise power delay profiling with commodity WiFi. IEEE Trans. Mobile Comput. PP(99), 1 (2015)Google Scholar
  11. 11.
    Shan, T.J., Wax, M., Kailath, T.: On spatial smoothing for direction-of-arrival estimation of coherent signals. IEEE Trans. Acoust. Speech Sign. Process. 33(4), 806–811 (1985)CrossRefGoogle Scholar
  12. 12.
    Tian, Z., Li, Z., Zhou, M., Jin, Y., Wu, Z.: PILA: sub-meter localization using CSI from commodity Wi-Fi devices. Sensors 16(10), 1664 (2016)CrossRefGoogle Scholar
  13. 13.
    Wang, J., Wang, H.T., Zhao, Y.: Direction finding in frequency-modulated-based passive bistatic radar with a four-element Adcock antenna array. IET Radar Sonar Navig. 5(8), 807–813 (2011) CrossRefGoogle Scholar
  14. 14.
    Wang, W., Liu, A. X., Shahzad, M., Ling, K., Lu, S.: Understanding and modeling of WiFi signal based human activity recognition. In: International Conference on Mobile Computing & Networking (2015)Google Scholar
  15. 15.
    Qian, K., Wu, C., Zhou, Z., Zheng, Y., Yang, Z., Liu, Y.: Inferring motion direction using commodity Wi-Fi for interactive exergames. In: CHI Conference (2017)Google Scholar
  16. 16.
    Wang, S., Jackson, B.R., Inkol, R.: Hybrid RSS/AOA emitter location estimation based on least squares and maximum likelihood criteria. In: Communications (2012)Google Scholar
  17. 17.
    Bharadia, D., Joshi, K.R., Kotaru, M., Katti, S.: BackFi: high throughput WiFi backscatter. ACM SIGCOMM Comput. Commun. Rev. 45(5), 283–296 (2015)CrossRefGoogle Scholar
  18. 18.
    Halperin, D., Hu, W., Sheth, A., Wetherall, D.: Tool release: gathering 802.11n traces with channel state information. ACM SIGCOMM Comput. Commun. Rev. 41(1), 53 (2011)CrossRefGoogle Scholar
  19. 19.
    Wang, J., Jiang, H., Xiong, J., Jamieson, K., Xie, B.: LiFS: low human-effort, device-free localization with fine-grained subcarrier information. In: International Conference on Mobile Computing & Networking (2016)Google Scholar
  20. 20.
    Xiao, J., Wu, K., Yi, Y., Wang, L., Ni, L.M.: Pilot: passive device-free indoor localization using channel state information (2013)Google Scholar
  21. 21.
    Seifeldin, M., Youssef, M.: Nuzzer: a large-scale device-free passive localization system for wireless environments. IEEE Trans. Mobile Comput. 12(7), 1321–1334 (2013)CrossRefGoogle Scholar
  22. 22.
    Qian, K., Wu, C., Zhang, Y., Zhang, G., Yang, Z., Liu, Y.: Widar2.0: passive human tracking with a single Wi-Fi link. In: Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys 2018), pp. 350–361. ACM, New York (2018).
  23. 23.
    Zhang, L., Gao, Q., Ma, X., Wang, J., Yang, T., Wang, H.: DeFi: robust training-free device-free wireless localization with WiFi. IEEE Trans. Veh. Technol. 67(9), 8822–8831 (2018). Scholar
  24. 24.
    Niu, J., Wang, B., Lei, S., Duong, T.Q., Chen, Y.: ZIL: an energy-efficient indoor localization system using ZigBee radio to detect WiFi fingerprints. IEEE J. Sel. Areas Commun. 33(7), 1431–1442 (2015)CrossRefGoogle Scholar
  25. 25.
    Habaebi, M.H., Khamis, R.O., Zyoud, A., Islam, M.R.: RSS based localization techniques for ZigBee wireless sensor network. In: International Conference on Computer & Communication Engineering (2014)Google Scholar
  26. 26.
    Zhu, J., Luo, H., Chen, Z., Li, Z.: RSSI based Bluetooth low energy indoor positioning. In: International Conference on Indoor Positioning & Indoor Navigation (2015)Google Scholar
  27. 27.
    Rida, M.E., Liu, F., Jadi, Y., Algawhari, A.A.A., Askourih, A.: Indoor location position based on Bluetooth signal strength. In: International Conference on Information Science & Control Engineering (2015)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020

Authors and Affiliations

  • Zengshan Tian
    • 1
  • Weiqin Yang
    • 1
    Email author
  • Yue Jin
    • 1
  • Gongzhui Zhang
    • 1
  1. 1.School of Communication and Information EngineeringChongqing University of Posts and TelecommunicationsChongqingChina

Personalised recommendations