Skip to main content

Advertisement

Springer Nature Link
Log in

DFPhaseFL: a robust device-free passive fingerprinting wireless localization system using CSI phase information

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Device-free passive wireless indoor localization is attracting great interest in recent years due to the widespread deployment of Wi-Fi devices and the numerous location-based services requirements. In this paper, we propose DFPhaseFL, the first device-free fingerprinting indoor localization system that purely uses CSI phase information. It utilizes the CSI phase information extracted from simply a single link to estimate the location of the target, neither requiring the target to wear any electronic equipment nor deploying a large number of access points and monitor devices. In DFPhaseFL, the raw CSI phases are extracted from the CSI measurements through the three antennas of the Intel WiFi Link 5300 wireless Network Interface Card (IWL 5300 NIC) firstly. Then, linear transformation and noise filtering are applied to acquire the calibrated CSI phases. Through experimental observations, we find that the calibrated CSI phase owns an unpredictable characteristic over time. Thus, it cannot be directly applied as a fingerprint. To this end, a transfer deep supervised neural network method combining deep neural network and transfer learning is proposed to obtain feature representations with both transferability and discriminability as fingerprints. Then, the DFPhaseFL system uses the SVM algorithm to obtain the estimation of the target location online. Experiment results demonstrate that the DFPhaseFL owns a better estimation precision compared with the other state of art, and maintain a stable localization accuracy for a long time without reacquiring the fingerprint database.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Zhou Z, Wu C, Yang Z, Liu Y (2015) Sensorless sensing with WiFi. Tsinghua Sci Technol 20(1):1–6

    Article  Google Scholar 

  2. Savazzi S, Sigg S, Nicoli M, Rampa V, Kianoush S, Spagnolini U (2016) Device-free radio vision for assisted living: leveraging wireless channel quality information for human sensing. IEEE Signal Proc Mag 33(2):45–58

    Article  Google Scholar 

  3. Youssef M, Mah M, Agrawala A (2007) Challenges: device-free passive localization for wireless environments. In: Proceedings of the 13th annual ACM international conference on Mobile computing and networking, ACM, pp 222–229.

  4. Halperin D, Hu W, Sheth A, Wetherall D (2011) Tool release: Gathering 802.11 n traces with channel state information. ACM SIGCOMM Comput Commun 41(1):53

    Article  Google Scholar 

  5. Abdel-Nasser H, Samir R, Sabek I, Youssef M (2013) MonoPHY: mono-stream-based device-free WLAN localization via physical layer information. In: Proceeding of the IEEE wireless communications and networking conference (WCNC), IEEE, pp 4546–4551

  6. Wang J, Jiang H, Xiong J, Jamieson K, Chen X, Fang D, Wang C (2018) Low human-effort, device-free localization with fine-grained subcarrier information. IEEE Trans Mobile Comput 17:2550–2563

    Article  Google Scholar 

  7. Li X, Li S, Zhang D, Xiong J, Wang Y, Mei H (2016) Dynamic-MUSIC: accurate device-free indoor localization. In: Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing, ACM, pp 196–207

  8. Qian K, Wu C, Yang Z, Yang C, Liu Y (2016) Decimeter level passive tracking with WiFi. In: Proceedings of the 3rd workshop on hot topics in wireless, ACM, pp 44–48

  9. Sabek I, Youssef M (2013) MonoStream: a minimal-hardware high accuracy device-free WLAN localization system. arXiv preprint. arXiv:1308.0768

  10. Xiao J, Wu K, Yi Y, Wang L, Ni LM (2013) Pilot: passive device-free indoor localization using channel state information. In: Proceeding of the IEEE 33rd international conference on distributed computing systems, IEEE, pp 236–245

  11. Zhou R, Lu X, Zhao P, Chen J (2017) Device-free presence detection and localization with SVM and CSI fingerprinting. IEEE Sens J 17(23):7990–7999

    Article  Google Scholar 

  12. Zhou R, Hao M, Lu X, Tang M, Fu Y (2018) Device-free localization based on CSI fingerprints and deep neural networks. In: Proceeding of the 15th annual IEEE international conference on sensing, communication, and networking (SECON), IEEE, pp 1–9

  13. Shi S, Sigg S, Chen L, Ji Y (2018) Accurate location tracking from CSI-based passive device-free probabilistic fingerprinting. IEEE T Veh Technol 67(6):5217–5230

    Article  Google Scholar 

  14. Gong L, Yang W, Man D, Dong G, Yu M, Lv J (2015) WiFi-based real-time calibration-free passive human motion detection. Sensors 15(12):32213–32229

    Article  Google Scholar 

  15. Vasisht D, Kumar S, Katabi D (2016) Decimeter-level localization with a single WiFi access point. In: Proceeding of the 13th USENIX symposium on networked systems design and implementation (NSDI), USENIX, pp 165–178

  16. Kotaru M, Joshi K, Bharadia D, Katti S (2015) Spotfi: decimeter level localization using WiFi. In: Proceedings of the 2015 ACM conference on special interest group on data communication (SIGCOMM), ACM, pp 269–282

  17. Xiong J, Sundaresan K, Jamieson K (2015) ToneTrack: leveraging frequency-agile radios for time-based indoor wireless localization. In: Proceedings of the 21st annual international conference on mobile computing and networking (MobiCom), ACM, pp 537–549

  18. Gjengset J, Xiong J, McPhillips G, Jamieson K (2014) Phaser: enabling phased array signal processing on commodity WiFi access points. In: Proceedings of the 20st annual international conference on mobile computing and networking (MobiCom), pp 153–164

  19. Sen S, Lee J, Kim K-H, Congdon P (2013) Avoiding multipath to revive inbuilding WiFi localization. In: Proceeding of the 11th annual international conference on Mobile systems, applications, and services (MobiSys), ACM, pp 249–262

  20. Xiong J, Jamieson K (2013) ArrayTrack: a fine-grained indoor location system. In: Proceeding of the 10th USENIX symposium on networked systems design and implementation (NSDI)

  21. Wang X, Gao L, Mao S (2016) CSI phase fingerprinting for indoor localization with a deep learning approach. IEEE Internet Things J 3(6):1113–1123

    Article  Google Scholar 

  22. Gao Q, Wang J, Ma X, Feng X, Wang H (2017) CSI-based device-free wireless localization and activity recognition using radio image features. IEEE T Veh Technol 66(11):10346–10356

    Article  Google Scholar 

  23. Si S, Tao D, Geng B (2010) Bregman divergence-based regularization for transfer subspace learning. IEEE T Knowl Data En 22(7):929–942

    Article  Google Scholar 

  24. Banerjee S, Merugu IS, Dhillon J (2005) Ghosh, clustering with bregman divergences. J Mach Learn Res 6(4):1705–1749

    MathSciNet  MATH  Google Scholar 

  25. Gong B, Shi Y, Sha F, Grauman K (2012) Geodesic flow kernel for unsupervised domain adaptation. In: 2012 IEEE conference on computer vision and pattern recognition, IEEE, pp 2066–2073

  26. Pan SJ, Tsang IW, Kwok JT, Yang Q (2011) Domain adaptation via transfer component analysis. IEEE Trans Neural Netw 22(2):199–210

    Article  Google Scholar 

  27. Borgwardt KM, Gretton A, Rasch MJ, Kriegel HP, Schölkopf B, Smola AJ (2006) Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics 22(14):e49–e57

    Article  Google Scholar 

  28. Long M, Wang J, Ding G, Sun J, Yu PS (2013) Transfer feature learning with joint distribution adaptation. In: 2013 IEEE international conference on computer vision, IEEE, pp 2200–2207

  29. Rao X, Li Z (2019) MSDFL: a robust minimal hardware low-cost device-free WLAN localization system. Neural Comput Appl 31(12):9261–9278

    Article  Google Scholar 

  30. Hu J, Lu J, Tan Y (2015) Deep transfer metric learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), IEEE, pp 325–333

  31. Suykens JA, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300

    Article  Google Scholar 

  32. Platt J (1998) Sequential minimal optimization: a fast algorithm for training support vector machines. In: Technical report MSR-TR-98-14, Microsoft Research

  33. Chang CC, Lin CJ (2011) LIBSVM: A library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27

    Google Scholar 

  34. Balas VE, Roy SS, Sharma D, Samui P (2019) Handbook of deep learning applications, vol 136. Springer, New York

    Book  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 61673310 and 61703324.

Author information

Authors and Affiliations

  1. Center for Complex Intelligent Networks, School of Mechano-Electronic Engineering, Xidian University, Xi’an, 710071, China

    Xinping Rao, Zhi Li, Yanbo Yang & Shengyang Wang

Authors
  1. Xinping Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yanbo Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shengyang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhi Li.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rao, X., Li, Z., Yang, Y. et al. DFPhaseFL: a robust device-free passive fingerprinting wireless localization system using CSI phase information. Neural Comput & Applic 32, 14909–14927 (2020). https://doi.org/10.1007/s00521-020-04847-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-04847-1

Keywords

Search

Navigation