MSDFL: a robust minimal hardware low-cost device-free WLAN localization system


The research on fingerprint-based device-free (DF) indoor localization has attracted great interest due to the ubiquitous of radio frequency signals and its accuracy. Most such approaches typically require a large number of transmitters and receivers to achieve acceptable accuracy, and there is another problem that the accuracy is degraded due to the expiration of the fingerprint database over time. In this paper, we present an accurate fingerprint-based device-free localization system using only a single stream over time, termed MSDFL. By analyzing the CSI fingerprint patterns and comparing the matrix similarity between CSI fingerprints and testing CSI samples matrix, the proposed MS algorithm is able to provide accurate DF localization with only a single stream. To cope with the noisy CSI samples and remove the subcarriers greatly affected by multipath, a novel CSI pre-processing scheme is applied on CSI data to reduce the noise and extract the most contributing subcarriers. In addition, to overcome the decrease in positioning accuracy caused by the expiration of the fingerprint database, a rigorously designed update scheme using artificial neural network is utilized for the renewal of fingerprinting database, which further enhance the performance of our system over time. Experimental results are presented to confirm that MSDFL can effectively improve location accuracy, compared with the other existing methods in representative indoor environment.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11


  1. 1.

    Hegarty CJ, Chatre E (2008) Evolution of the global navigation satellite system (GNSS). Proc IEEE 96(12):1902–1917

    Article  Google Scholar 

  2. 2.

    Prasithsangaree P, Krishnamurthy P, Chrysanthis P (2002) On indoor position location with wireless LANs. In: Personal, indoor and mobile radio communications, 2002. The 13th IEEE international symposium, IEEE, vol 2, pp 720–724, September 2002

  3. 3.

    Youssef M, Agrawala A (2005) The Horus WLAN location determination system. In: Proceedings of the 3rd international conference on mobile systems, applications, and services, ACM, pp 205–218, June 2005

  4. 4.

    Ni LM, Liu Y, Lau YC, Patil AP (2004) LANDMARC: indoor location sensing using active RFID. Wirel Netw 10(6):701–710

    Article  Google Scholar 

  5. 5.

    Chon HD, Jun S, Jung H, An SW (2004) Using RFID for accurate positioning. Positioning 1(8):0

    Google Scholar 

  6. 6.

    Brumitt B, Meyers B, Krumm J, Kern A, Shafer S (2000) Easy living: technologies for intelligent environments. In: International symposium on handheld and ubiquitous computing. Springer, Berlin, Heidelberg, pp 12–29, September 2000

    Google Scholar 

  7. 7.

    Harter A, Hopper A (1994) A distributed location system for the active office. IEEE Netw 8(1):62–70

    Article  Google Scholar 

  8. 8.

    Want R, Hopper A, Falcao V, Gibbons J (1992) The active badge location system. ACM Trans Inf Syst (TOIS) 10(1):91–102

    Article  Google Scholar 

  9. 9.

    Priyantha NB, Chakraborty A, Balakrishnan H (2000) The cricket location-support system. In: Proceedings of the 6th annual international conference on mobile computing and networking, ACM, pp 32–43, August 2000

  10. 10.

    Gu Y, Lo A, Niemegeers I (2009) A survey of indoor positioning systems for wireless personal networks. IEEE Commun Surv Tutor 11(1):13–32

    Article  Google Scholar 

  11. 11.

    Li B, Wang Y, Lee HK, Dempster A, Rizos C (2005) Method for yielding a database of location fingerprints in WLAN. IEE Proc Commun 152(5):580–586

    Article  Google Scholar 

  12. 12.

    Mazuelas S, Bahillo A, Lorenzo RM, Fernandez P, Lago FA, Garcia E, Abril EJ (2009) Robust indoor positioning provided by real-time RSSI values in unmodified WLAN networks. IEEE J Sel Top Signal Process 3(5):821–831

    Article  Google Scholar 

  13. 13.

    Ciurana M, Barceló F, Cugno S (2006) Indoor tracking in WLAN location with TOA measurements. In: Proceedings of the 4th ACM international workshop on mobility management and wireless access, ACM, pp 121–125, October 2006

  14. 14.

    Yamasaki R, Ogino A, Tamaki T, Uta T, Matsuzawa N, Kato T (2005) TDOA location system for IEEE 802.11b WLAN. In: Wireless communications and networking conference, 2005, IEEE, vol 4, pp 2338–2343, March 2005

  15. 15.

    Wong C, Klukas R, Messier GG (2008) Using WLAN infrastructure for angle-of-arrival indoor user location. In: 2008 IEEE 68th vehicular technology conference, VTC 2008-Fall, IEEE, pp 1–5, September 2008

  16. 16.

    Bahl P, Padmanabhan VN (2000) RADAR: an in-building RF-based user location and tracking system. In: INFOCOM 2000. Nineteenth annual joint conference of the IEEE computer and communications societies, proceedings, IEEE, vol 2, pp 775–784

  17. 17.

    Xu Y, Zhou M, Meng W, Ma L (2010) Optimal KNN positioning algorithm via theoretical accuracy criterion in WLAN indoor environment. In: 2010 IEEE global telecommunications conference (GLOBECOM 2010), IEEE, pp 1–5, December 2010

  18. 18.

    Tran Q, Tantra JW, Foh CH, Tan AH, Yow KC, Qiu D (2006) Wireless indoor positioning system with enhanced nearest neighbors in signal space algorithm. In: 2006 IEEE 64th vehicular technology conference, 2006, VTC-2006 Fall, IEEE, pp 1–5, September 2006

  19. 19.

    Zhou M, Xu Y, Tang L (2010) Multilayer ANN indoor location system with area division in WLAN environment. J Syst Eng Electron 21(5):914–926

    Article  Google Scholar 

  20. 20.

    Brunato M, Battiti R (2005) Statistical learning theory for location fingerprinting in wireless LANs. Comput Netw 47(6):825–845

    Article  Google Scholar 

  21. 21.

    Teuber A, Eissfeller B, Pany T (2006) A two-stage fuzzy logic approach for wireless LAN indoor positioning. In: Proceedings of IEEE/ION position location and navigation symposium, vol 4, pp 730–738, April 2006

  22. 22.

    Pan SJ, Kwok JT, Yang Q, Pan JJ (2007) Adaptive localization in a dynamic WiFi environment through multi-view learning. In: AAAI, pp 1108–1113, July 2007

  23. 23.

    Yin J, Yang Q, Ni LM (2008) Learning adaptive temporal radio maps for signal-strength-based location estimation. IEEE Trans Mob Comput 7(7):869–883

    Article  Google Scholar 

  24. 24.

    Yin J, Yang Q, Ni L (2005) Adaptive temporal radio maps for indoor location estimation. In: Third IEEE international conference on pervasive computing and communications, 2005, PerCom 2005, IEEE, pp 85–94, March 2005

  25. 25.

    Liu J, Zhang Y, Zhao F (2006) Robust distributed node localization with error management. In: Proceedings of the 7th ACM international symposium on mobile ad hoc networking and computing, ACM, pp 250–261, May 2006

  26. 26.

    Moore D, Leonard J, Rus D, Teller S (2004) Robust distributed network localization with noisy range measurements. In: Proceedings of the 2nd international conference on embedded networked sensor systems, ACM, pp 50–61, November 2004

  27. 27.

    Zhang D, Ma J, Chen Q, Ni LM (2007) An RF-based system for tracking transceiver-free objects. In: Fifth annual IEEE international conference on pervasive computing and communications, 2007, PerCom’07, IEEE, pp 135–144, March 2007

  28. 28.

    Zhang D, Ni LM (2009) Dynamic clustering for tracking multiple transceiver-free objects. In: IEEE international conference on pervasive computing and communications, 2009, PerCom 2009, IEEE, pp 1–8, March 2009

  29. 29.

    Wu K, Xiao J, Yi Y, Chen D, Luo X, Ni LM (2013) CSI-based indoor localization. IEEE Trans Parallel Distrib Syst 24(7):1300–1309

    Article  Google Scholar 

  30. 30.

    Halperin D, Hu W, Sheth A, Wetherall D (2010) Predictable 802.11 packet delivery from wireless channel measurements. In: ACM SIGCOMM computer communication review, ACM, vol 40, no 4, pp 159–170, August 2010

    Article  Google Scholar 

  31. 31.

    Wang X, Gao L, Mao S, Pandey S (2015) DeepFi: deep learning for indoor fingerprinting using channel state information. In: 2015 IEEE wireless communications and networking conference (WCNC), IEEE, pp 1666–1671, March 2015

  32. 32.

    Wang X, Gao L, Mao S (2015) PhaseFi: phase fingerprinting for indoor localization with a deep learning approach. In: 2015 IEEE global communications conference (GLOBECOM), IEEE, pp 1–6, December 2015

  33. 33.

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

  34. 34.

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

  35. 35.

    Wang J, Jiang H, Xiong J, Jamieson K, Chen X, Fang D, Xie B (2016) LiFS: low human-effort, device-free localization with fine-grained subcarrier information. In: Proceedings of the 22nd annual international conference on mobile computing and networking, ACM, pp 243–256, October 2016

  36. 36.

    Wu K, Xiao J, Yi Y, Gao M, Ni LM (2012) FILA: fine-grained indoor localization. In: INFOCOM, 2012 proceedings IEEE, pp 2210–2218, March 2012

  37. 37.

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

    Article  Google Scholar 

  38. 38.

    Rodriguez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344(6191):1492–1496

    Article  Google Scholar 

  39. 39.

    Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533

    Article  Google Scholar 

  40. 40.

    Outemzabet S, Nerguizian C (2008) Accuracy enhancement of an indoor ANN-based fingerprinting location system using particle filtering and a low-cost sensor. In: IEEE vehicular technology conference, 2008, VTC Spring 2008, IEEE, pp 2750–2754, May 2008

  41. 41.

    Sharaf R, Noureldin A (2007) Sensor integration for satellite-based vehicular navigation using neural networks. IEEE Trans Neural Netw 18(2):589–594

    Article  Google Scholar 

  42. 42.

    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, September 2007

  43. 43.

    Moussa M, Youssef M (2009) Smart devices for smart environments: device-free passive detection in real environments. In: IEEE international conference on pervasive computing and communications, 2009, PerCom 2009, IEEE, pp 1–6, March 2009

Download references


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

Author information



Corresponding author

Correspondence to Zhi Li.

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

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation


  • Channel state information (CSI)
  • Fingerprinting
  • Indoor localization
  • Wi-Fi