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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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.2017.2671454
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.2017.1700133
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.2017.2784387
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.2015.7040509
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.2013.243
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.2012.134
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.2015.2432038
Candes, E., Wakin, M.: An introduction to compressive sampling. IEEE Signal Process. Mag. 25(2), 21–30 (2008). https://doi.org/10.1109/msp.2007.914731
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
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.2016.2567396
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.2017.2649540
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.2007.894265
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.2015.2476495
Ji, S., Xue, Y., Carin, L.: Bayesian compressive sensing. IEEE Trans. Signal Process. 56(6), 2346–2356 (2008). https://doi.org/10.1109/TSP.2007.914345
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Yu, D., Guo, Y., Li, N., Yang, S. (2019). Aggregating Multidimensional Wireless Link Information for Device-Free Localization. In: Liu, X., Cheng, D., Jinfeng, L. (eds) Communications and Networking. ChinaCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 262. Springer, Cham. https://doi.org/10.1007/978-3-030-06161-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-06161-6_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-06160-9
Online ISBN: 978-3-030-06161-6
eBook Packages: Computer ScienceComputer Science (R0)