Device-free passive wireless localization system with transfer deep learning method

  • Xinping Rao
  • Zhi LiEmail author
  • Yanbo Yang
Original Research


Recently, device-free passive wireless indoor localization (DFPWL) has attracted great interest due to the widespread deployment of Wi-Fi devices and the rapid growth in demand for location-based services (LBS). The DFPWL fingerprinting approach based on channel state information (CSI) has become the mainstream method since its simple deployment and localization accuracy. It determines the location of the target from the new measurement CSI by collecting a training database that measures the CSI and using a machine learning classifier. However, we have found through experiments that even if the indoor environment does not change, the CSI fingerprint will be different from the CSI fingerprint in the database over time, and most of the CSI-based DFPWL fingerprinting method ignores this. To cope with the reduction in localization accuracy caused by the time-varying characteristic of CSI, we propose a novel transfer deep learning-based DFPWL system in this paper. It uses the CSI extracted from 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 APs and Monitor Devices. Unlike the other traditional CSI-based DFPWL fingerprinting approaches using the pre-processed CSI samples as fingerprints, our system utilizes the transfer deep learning (TDL) method to learn new feature representations from the CSI samples as fingerprints, which can simultaneously minimize the intra-class differences, maximize inter-class differences, and minimize the distribution differences between fingerprint database and test samples. Finally, the KNN algorithm is utilized to compare the test samples and the fingerprint database under the new feature representation to obtain the estimation location of the target. Experiment results show that our system can effectively improve localization accuracy compared to the other state-of-art, and can maintain stable localization accuracy for a long time without reacquiring the CSI fingerprint database.


Device-free indoor localization Channel state information (CSI) Deep learning Transfer learning 



This work was supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 61673310 and 61703324, and the Natural Science Foundation of Shaanxi Province under Grant No. 2019JQ-215.

Compliance with ethical standards

Conflict of interest

We declare that we have no conflict of interest.


  1. Abadi M, Agarwal A, Barham P et al (2016) Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv Prepr arXiv160304467Google Scholar
  2. Abdel-Nasser H, Samir R, Sabek I, Youssef M (2013) MonoPHY: mono-stream-based device-free WLAN localization via physical layer information. IEEE Wirel Commun Netw Conf WCNC. CrossRefGoogle Scholar
  3. Banerjee A, Merugu S, Dhillon IS, Ghosh J (2005) Clustering with Bregman divergences. J Mach Learn Res 6:1705–1749MathSciNetzbMATHGoogle Scholar
  4. Borgwardt KM, Gretton A, Rasch MJ et al (2006) Integrating structured biological data by Kernel maximum mean discrepancy. Bioinformatics 22:e49–e57. CrossRefGoogle Scholar
  5. Chen H, Zhang Y, Li W et al (2017) ConFi: convolutional neural networks based indoor wi-fi localization using channel state information. IEEE Access 5:18066–18074. CrossRefGoogle Scholar
  6. Ciabattoni L, Foresi G, Monteriù A et al (2019) Real time indoor localization integrating a model based pedestrian dead reckoning on smartphone and BLE beacons. J Ambient Intell Humaniz Comput 10:1–12CrossRefGoogle Scholar
  7. Cui Z, Chang H, Shan S, Chen X (2014) Generalized unsupervised manifold alignment. Adv Neural Inf Process Syst 3:2429–2437Google Scholar
  8. Fernando B, Habrard A, Sebban M, Tuytelaars T (2013) Unsupervised visual domain adaptation using subspace alignment. In: Proceedings of the IEEE International Conference on Computer Vision. pp 2960–2967Google Scholar
  9. Fu B, Kirchbuchner F, von Wilmsdorff J et al (2019) Performing indoor localization with electric potential sensing. J Ambient Intell Humaniz Comput 10:731–746CrossRefGoogle Scholar
  10. Gao Q, Wang J, Ma X et al (2017) CSI-based device-free wireless localization and activity recognition using radio image features. IEEE Trans Veh Technol 66:10346–10356. CrossRefGoogle Scholar
  11. Gong B, Shi Y, Sha F, Grauman K (2012) Geodesic flow kernel for unsupervised domain adaptation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. pp 2066–2073Google Scholar
  12. Halperin D, Hu W, Sheth A, Wetherall D (2011) Tool release: gathering 802.11 n traces with channel state information. ACM SIGCOMM Comput Commun Rev 41:53CrossRefGoogle Scholar
  13. Han S, Li Y, Meng W et al (2018) Indoor localization with a single Wi-Fi access point based on OFDM-MIMO. IEEE Syst J 13:964–972CrossRefGoogle Scholar
  14. Hosen ASMS, Park JS, Cho GH (2015) Utilizing the virtual triangulation for wireless indoor localization of mobile devices with channel state information. Int J Multimed Ubiquitous Eng 10:265–276. CrossRefGoogle Scholar
  15. Hu J, Lu J, Tan Y-P (2015) Deep transfer metric learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 325–333Google Scholar
  16. Lee S, Moon N (2018) Location recognition system using random forest. J Ambient Intell Humaniz Comput 9:1191–1196CrossRefGoogle Scholar
  17. Li X, Li S, Zhang D et al (2016) Dynamic-MUSIC: accurate device-free indoor localization. In: UbiComp’16. pp 196–207Google Scholar
  18. Long M, Wang J, Ding G et al (2013) Transfer feature learning with joint distribution adaptation. In: Proceedings of the IEEE International Conference on computer vision. pp 2200–2207Google Scholar
  19. Mager B, Lundrigan P, Patwari N (2015) Fingerprint-based device-free localization performance in changing environments. IEEE J Sel Areas Commun 33:2429–2438. CrossRefGoogle Scholar
  20. Ohara K, Maekawa T, Kishino Y et al (2015) Transferring positioning model for device-free passive indoor localization. In: UbiComp 2015—Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM Press, New York, New York, USA, pp 885–896Google Scholar
  21. Pan SJ, Tsang IW, Kwok JT, Yang Q (2010) Domain adaptation via transfer component analysis. IEEE Trans Neural Networks 22:199–210CrossRefGoogle Scholar
  22. Qian K, Wu C, Yang Z et al (2014) PADS: Passive detection of moving targets with dynamic speed using PHY layer information. In: 2014 20th IEEE International Conference on parallel and distributed systems (ICPADS). IEEE, pp 1–8Google Scholar
  23. Qian K, Wu C, Yang Z et al (2016) Decimeter level passive tracking with WiFi. In: Proceedings of the Annual International Conference on mobile computing and networking, MOBICOM. pp 44–48Google Scholar
  24. Rao X, Li Z (2019) MSDFL: a robust minimal hardware low-cost device-free WLAN localization system. Neural Comput Appl 31:9261–9278. CrossRefGoogle Scholar
  25. Sabek I, Youssef M (2013) MonoStream: a minimal-hardware high accuracy device-free WLAN localization systemGoogle Scholar
  26. Samui P, Roy SS, Balas VE (2017) Handbook of neural computation. Academic Press, CambridgeGoogle Scholar
  27. Savazzi S, Sigg S, Nicoli M et al (2016) Device-free radio vision for assisted living: leveraging wireless channel quality information for human sensing. IEEE Signal Process Mag 33:45–58. CrossRefGoogle Scholar
  28. Shi S, Sigg S, Chen L, Ji Y (2018) Accurate location tracking from CSI-based passive device-free probabilistic fingerprinting. IEEE Trans Veh Technol 67:5217–5230. CrossRefGoogle Scholar
  29. Si S, Tao D, Geng B (2009) Bregman divergence-based regularization for transfer subspace learning. IEEE Trans Knowl Data Eng 22:929–942CrossRefGoogle Scholar
  30. Tuia D, Volpi M, Trolliet M, Camps-Valls G (2014) Semisupervised manifold alignment of multimodal remote sensing images. IEEE Trans Geosci Remote Sens 52:7708–7720. CrossRefGoogle Scholar
  31. Wang X, Gao L, Mao S (2016a) CSI phase fingerprinting for indoor localization with a deep learning approach. IEEE Internet Things J 3:1113–1123. CrossRefGoogle Scholar
  32. Wang X, Gao L, Mao S, Pandey S (2016b) CSI-based fingerprinting for indoor localization: a deep learning approach. IEEE Trans Veh Technol. CrossRefGoogle Scholar
  33. Wang X, Gao L, Mao S, Pandey S (2017a) CSI-based fingerprinting for indoor localization: a deep learning approach. IEEE Trans Veh Technol 66:763–776. CrossRefGoogle Scholar
  34. Wang X, Wang X, Mao S (2017b) CiFi: Deep convolutional neural networks for indoor localization with 5 GHz Wi-Fi. In: 2017 IEEE International Conference on Communications (ICC). IEEE, pp 1–6Google Scholar
  35. Wang J, Xiong J, Jiang H et al (2018) Low human-effort, device-free localization with fine-grained subcarrier information. IEEE Trans Mob Comput 17:2550–2563. CrossRefGoogle Scholar
  36. Wu K, Jiang X, Yi Y et al (2012a) FILA: Fine-grained indoor localization. Proc—IEEE INFOCOM 2210–2218Google Scholar
  37. Wu K, Xiao J, Yi Y et al (2012b) CSI-based indoor localization. IEEE Trans Parallel Distrib Syst 24:1300–1309CrossRefGoogle Scholar
  38. Wu K, Xiao J, Yi Y et al (2012c) FILA: fine-grained indoor localization. In: 2012 Proceedings IEEE INFOCOM. pp 2210–2218Google Scholar
  39. Wu C, Yang Z, Zhou Z et al (2015) PhaseU: real-time LOS identification with WiFi. In: Proceedings—IEEE INFOCOM. pp 2038–2046Google Scholar
  40. Xiao J, Wu K, Yi Y et al (2013) Pilot: passive device-free indoor localization using channel state information. Proc Int Conf Distrib Comput Syst. CrossRefGoogle Scholar
  41. Xie Y, Li Z, Li M (2019) Precise power delay profiling with commodity wi-fi. IEEE Trans Mob Comput 18:1342–1355. CrossRefGoogle Scholar
  42. Farajidavar N, de Campos T, Kittler J (2014) Transductive transfer machine. In: Asian Conference on Computer Vision. Springer, Berlin pp 623–639CrossRefGoogle Scholar
  43. Youssef M, Mah M, Agrawala A (2007) Challenges: device-free passive localization for wireless environments. Proc Annu Int Conf Mob Comput Networking, MOBICOM. CrossRefGoogle Scholar
  44. Zhou Z, Yang Z, Wu C et al (2014) LiFi: Line-Of-Sight identification with WiFi. In: Proceedings—IEEE INFOCOM. IEEE, pp 2688–2696Google Scholar
  45. Zhou Z, Wu C, Yang Z, Liu Y (2015) Sensorless sensing with WiFi. Tsinghua Sci Technol 20:1–6. CrossRefGoogle Scholar
  46. Zhou R, Lu X, Zhao P, Chen J (2017) Device-free presence detection and localization with SVM and CSI fingerprinting. IEEE Sens J 17:7990–7999. CrossRefGoogle Scholar
  47. Zhou R, Hao M, Lu X et al (2018) Device-free localization based on CSI fingerprints and deep neural networks. 2018 15th Annu IEEE Int Conf sensing. Commun Netw SECON 2018:1–9. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Center for Complex Intelligent Networks, School of Mechano-electronic EngineeringXidian UniversityXi’anChina

Personalised recommendations