Indoor passive localisation based on reliable CSI extraction

Indoor passive localisation based on reliable CSI extraction

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Communications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In indoor environment, passive human detection and localisation are important enabling technologies for elder healthcare, emergence rescue and target tracking applications. Recently, the fine-grained channel state information (CSI) of Wi-Fi was adopted for indoor localisation due to the low-cost Wi-Fi network interface card and available firmware modifications for CSI extraction. However, due to multipath fading and spatial-temporal dynamics of wireless channel, stable CSI extraction is a challenging task to achieve reliable CSI fingerprint matching. In this study, the sensitivity of CSI is first analysed and stable CSI fingerprints can be obtained by reducing the variance from interference and white noise. The stable CSI fingerprints are then classified by quadratic discriminant analysis to achieve location matching. Extensive experiments have been conducted to justify the system performance. The results reveal that the proposed indoor passive localisation system outperforms passive CSI-MIMO system in terms of performance.


    1. 1)
      • 1. Zhou, Z., Yang, Z., Wu, C., et al: ‘On multipath link characterization and adaptation for device-free human detection’. Proc. IEEE ICDCS 2015, Columbus, OH, USA, 2015.
    2. 2)
      • 2. Kotaru, M., Joshi, K., Bharadia, D., et al: ‘Spotfi: decimeter level localization using Wi-Fi’. Proc. ACM SigComm 2015, London, United Kingdom, August 2015.
    3. 3)
      • 3. Gao, L.: ‘Channel state information fingerprinting based indoor localization: a deep learning approach’. M.S. thesis, Auburn University, 2015.
    4. 4)
      • 4. Wang, X., Gao, L., Mao, S., et al: ‘Deepfi: deep learning for indoor fingerprinting using channel state information’. Proc. IEEE WCNC 2015, New Orleans, USA, 2015.
    5. 5)
      • 5. He, W., Wu, K., Zou, Y., et al: ‘Wig: Wi-Fi-based gesture recognition system’. Proc. IEEE ICCCN 2015, Las Vegas, NV, USA, 2015.
    6. 6)
      • 6. Gajić, B., Janković, N., Koprivica, S., et al: ‘Indoor localization of mobile robots using active LED beacons’. Proc. INFOTEH-JAHORINA, Jahorina, Bosnia and Herzegovina, 2016.
    7. 7)
      • 7. Karakaya, S., Ocak, H., Küçükyıldız, G., et al: ‘A hybrid indoor localization system based on infra-red imaging and odometry’. Proc. ICCVPR 2015, Las Vegas, NV, USA, 2015.
    8. 8)
      • 8. Medina, C., Segura, J.C., Torre, A.D.: ‘Ultrasound indoor positioning system based on a low-power wireless sensor network providing sub-centimeter accuracy’, Sensors, 2013, 13, (3), pp. 35013526.
    9. 9)
      • 9. Schröder, Y., Zengen, Y.V., Rottmann, S., et al: ‘Inphase: an indoor localization system based on phase difference measurements’. Proc. of the 1st KuVS Expert Talk on Localization, Lübeck, Germany, April, 2015.
    10. 10)
      • 10. Shao, C., Jing, X., Sun, S., et al: ‘Active RFID-based indoor localization algorithm using virtual reference through bivariate Newton interpolation’. Proc. ICCCN 2015, Las Vegas, NV, USA, 2015.
    11. 11)
      • 11. Vander, J., Tokekar, P., Isler, V.: ‘Cautious greedy strategy for bearing-only active localization: analysis and field experiments’, Filed Robot., 2014, 31, pp. 269318.
    12. 12)
      • 12. Luca, G., Roumeliotis, S.I.: ‘Active vision-based robot localization and navigation in a visual memory’. Proc. IEEE ICRA 2011, Shanghai, China, 2011.
    13. 13)
      • 13. Liang, J., Liu, D.: ‘Passive localization of mixed near-field and far-field sources using two-stage MUSIC algorithm’, IEEE Trans. Signal Process., 2015, 12, pp. 105118.
    14. 14)
      • 14. Saeed, A., Member, S., Kosba, A.E.: ‘Ichnaea: A low-overhead robust WLAN device-free passive localization system’, Signal Process., 2013, 99, pp. 111.
    15. 15)
      • 15. Xiao, J., Wu, K., Yi, Y., et al: ‘Pilot: passive device-free indoor localization using channel state information’. Proc. IEEE ICDCS, 2013, Columbus, USA, 2013.
    16. 16)
      • 16. Bahl, P., Padmanabhan, V.N.: ‘RADAR: an in-building RF-based user location and tracking system’. Proc. of IEEE INFOCOM, Tel Aviv, Israel, 2000.
    17. 17)
      • 17. Dayekh, S.: ‘Cooperative localization in mines using fingerprinting and neural networks’. M.S. thesis, Department Applied Sciences, Universite of Quebec en Abitibi-Temiscamingue, Canada, 2010.
    18. 18)
      • 18. Xiao, J., Wu, K.S., Yi, Y.W., et al: ‘FIFS: fine-grained indoor fingerprinting system’. Proc. IEEE ICCCN, 2012, Munich, Germany, 2012.
    19. 19)
      • 19. Sen, S., Radunovic, B., Choudhury, R.R., et al: ‘Spot localization using PHY layer information’. Proc. MobiSys, New York, USA, vol. 12, 2012.
    20. 20)
      • 20. Li, Z., Braun, T., Dimitrova, D.: ‘A passive Wi-Fi source localization system based on fine-grained power-based trilateration’. Proc. IEEE WoWMoM, Boston, MA, USA, 2015.
    21. 21)
      • 21. Loog, M., Duin, R., Haeb, R.: ‘Multiclass linear dimension reduction by weighted pairwise Fisher criteria’, IEEE Trans. Pattern Anal. Mach. Intell., 2001, 7, (23), pp. 762766.
    22. 22)
      • 22. Halperin, D., Hu, W., Sheth, A., et al: ‘Predictable 802.11 packet delivery from wireless channel measurements’. Proc. of ACM SIGCOMM, New Delhi, India, 2010.
    23. 23)
      • 23. Chapre, Y., Ignjatovic, A., Seneviratne, A., et al: ‘CSI-MIMO: an efficient Wi-Fi fingerprinting using channel state information with MIMO’, Pervasive Mob. Comput., 2015, 23, pp. 89103.

Related content

This is a required field
Please enter a valid email address