Advertisement

Aggregating Multidimensional Wireless Link Information for Device-Free Localization

  • Dongping Yu
  • Yan GuoEmail author
  • Ning Li
  • Sixing Yang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 262)

Abstract

Device-free localization (DFL) is an emerging and promising technique, which can realize target localization without the requirement of attaching any wireless devices to targets. By analyzing the shadowing loss caused by targets on wireless links, we can estimate the target locations. However, for existing DFL approaches, a large number of wireless links is required to guarantee a certain localization precision, which may lead to high hardware cost. In this paper, we propose a novel multi-target device-free localization method with multidimensional wireless link information (MDMI). Unlike previous works that measure RSS only on a single transmission power level, MDMI collects RSS measurements from multiple transmission power levels to enrich the measurement information. Furthermore, the compressive sensing (CS) theory is applied by exploiting the inherent spatial sparsity of DFL. We model the DFL problem as a joint sparse recovery problem and adopt the multiple sparse Bayesian learning (M-SBL) algorithm to reconstruct the sparse vectors of different transmission power levels. Numerical simulation results demonstrate the outstanding performance of the proposed method.

Keywords

Device-free localization Wireless sensor network Compressive sensing Sparse Bayesian learning 

Notes

Acknowledgment

This work was supported in part by the National Natural Science Foundation of China under grant 61871400, and 61571463; the Natural Science Foundation of Jiangsu Province under grant BK20171401.

References

  1. 1.
    Khalajmehrabadi, A., Gatsis, N., Akopian, D.: Modern WLAN fingerprinting indoor positioning methods and deployment challenges. IEEE Commun. Surv. Tuts. 19(3), 1974–2002 (2017).  https://doi.org/10.1109/COMST.2017.2671454CrossRefGoogle Scholar
  2. 2.
    Wang, J., Gao, Q., Pan, M., Fang, Y.: Device-free wireless sensing: challenges, opportunities, and applications. IEEE Netw. 32(2), 132–137 (2018).  https://doi.org/10.1109/mnet.2017.1700133CrossRefGoogle Scholar
  3. 3.
    Lei, Q., Zhang, H., Sun, H., Tang, L.: Fingerprint-based device-free localization in changing environments using enhanced channel selection and logistic regression. IEEE Access 66, 2569–2577 (2018).  https://doi.org/10.1109/ACCESS.2017.2784387CrossRefGoogle Scholar
  4. 4.
    Zhou, Z., Wu, C., Yang, Z., Liu, Y.: Sensorless sensing with WiFi. Tsinghua Sci. Technol. 20(1), 1–6 (2015).  https://doi.org/10.1109/tst.2015.7040509CrossRefGoogle Scholar
  5. 5.
    Zhang, D., et al.: Fine-grained localization for multiple transceiver-free objects by using RF-based technologies. IEEE Trans. Parallel Distrib. Syst. 25(6), 1464–1475 (2014).  https://doi.org/10.1109/tpds.2013.243CrossRefGoogle Scholar
  6. 6.
    Zhang, D., Liu, Y., Guo, X., Ni, L.: RASS: a real-time, accurate, and scalable system for tracking transceiver-free objects. IEEE Trans. Parallel Distrib. Syst. 24(5), 996–1008 (2013).  https://doi.org/10.1109/tpds.2012.134CrossRefGoogle Scholar
  7. 7.
    Wang, Q., Yigitler, H., Jantti, R., Huang, X.: Localizing multiple objects using radio tomographic imaging technology. IEEE Trans. Veh. Technol. 65(5), 3641–3656 (2016).  https://doi.org/10.1109/tvt.2015.2432038CrossRefGoogle Scholar
  8. 8.
    Candes, E., Wakin, M.: An introduction to compressive sampling. IEEE Signal Process. Mag. 25(2), 21–30 (2008).  https://doi.org/10.1109/msp.2007.914731CrossRefGoogle Scholar
  9. 9.
    Wang, J., Fang, D., Chen, X., Yang, Z., Xing, T., Cai, L.: LCS: compressive sensing based device-free localization for multiple targets in sensor networks. In: IEEE INFOCOM 2013, Turin, Italy, pp. 14–19 (2013).  https://doi.org/10.1109/infcom.2013.6566752
  10. 10.
    Wang, J., et al.: E-HIPA: an energy-efficient framework for high-precision multi-target-adaptive device-free localization. IEEE Trans. Mob. Comput. 16(3), 716–729 (2017).  https://doi.org/10.1109/tmc.2016.2567396CrossRefGoogle Scholar
  11. 11.
    Yu, D., Guo, Y., Li, N., Fang, D.: Dictionary refinement for compressive sensing based device-free localization via the variational EM algorithm. IEEE Access 4, 9743–9757 (2016).  https://doi.org/10.1109/access.2017.2649540CrossRefGoogle Scholar
  12. 12.
    Wipf, D., Rao, B.: An empirical Bayesian strategy for solving the simultaneous sparse approximation problem. IEEE Trans. Signal Process. 55(7), 3704–3716 (2007).  https://doi.org/10.1109/TSP.2007.894265MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Wang, J., Gao, Q., Pan, M., Zhang, X., Yu, Y., Wang, H.: Towards accurate device-free wireless localization with a saddle surface model. IEEE Trans. Veh. Technol. 65(8), 6665–6677 (2016).  https://doi.org/10.1109/tvt.2015.2476495CrossRefGoogle Scholar
  14. 14.
    Ji, S., Xue, Y., Carin, L.: Bayesian compressive sensing. IEEE Trans. Signal Process. 56(6), 2346–2356 (2008).  https://doi.org/10.1109/TSP.2007.914345MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.College of Communications EngineeringArmy Engineering University of PLANanjingChina

Personalised recommendations