Neural Computing and Applications

, Volume 31, Issue 12, pp 9261–9278 | Cite as

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

  • Xinping Rao
  • Zhi LiEmail author
Original Article


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.


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



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


  1. 1.
    Hegarty CJ, Chatre E (2008) Evolution of the global navigation satellite system (GNSS). Proc IEEE 96(12):1902–1917CrossRefGoogle 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 2002Google Scholar
  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 2005Google Scholar
  4. 4.
    Ni LM, Liu Y, Lau YC, Patil AP (2004) LANDMARC: indoor location sensing using active RFID. Wirel Netw 10(6):701–710CrossRefGoogle Scholar
  5. 5.
    Chon HD, Jun S, Jung H, An SW (2004) Using RFID for accurate positioning. Positioning 1(8):0Google 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 2000CrossRefGoogle Scholar
  7. 7.
    Harter A, Hopper A (1994) A distributed location system for the active office. IEEE Netw 8(1):62–70CrossRefGoogle Scholar
  8. 8.
    Want R, Hopper A, Falcao V, Gibbons J (1992) The active badge location system. ACM Trans Inf Syst (TOIS) 10(1):91–102CrossRefGoogle 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 2000Google Scholar
  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):13–32CrossRefGoogle 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):580–586CrossRefGoogle 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):821–831CrossRefGoogle 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 2006Google Scholar
  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 2005Google Scholar
  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 2008Google Scholar
  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 775–784Google Scholar
  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 2010Google Scholar
  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 2006Google Scholar
  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):914–926CrossRefGoogle Scholar
  20. 20.
    Brunato M, Battiti R (2005) Statistical learning theory for location fingerprinting in wireless LANs. Comput Netw 47(6):825–845CrossRefGoogle 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 2006Google Scholar
  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 2007Google Scholar
  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):869–883CrossRefGoogle 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 2005Google Scholar
  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 2006Google Scholar
  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 2004Google Scholar
  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 2007Google Scholar
  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 2009Google Scholar
  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):1300–1309CrossRefGoogle 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 2010CrossRefGoogle 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 2015Google Scholar
  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 2015Google Scholar
  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 2013Google Scholar
  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 2013Google Scholar
  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 2016Google Scholar
  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 2012Google Scholar
  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):53CrossRefGoogle Scholar
  38. 38.
    Rodriguez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344(6191):1492–1496CrossRefGoogle Scholar
  39. 39.
    Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533CrossRefGoogle 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 2008Google Scholar
  41. 41.
    Sharaf R, Noureldin A (2007) Sensor integration for satellite-based vehicular navigation using neural networks. IEEE Trans Neural Netw 18(2):589–594CrossRefGoogle 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 2007Google Scholar
  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 2009Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electro-Mechanical EngineeringXidian UniversityXi’anChina

Personalised recommendations