volume | PIER Journals

Abstract

In recent years, radio tomographic imaging (RTI) is an emerging device-free localization (DFL) technology enabling the localization of people and other objects without requiring them to carry any electronic device. Different from other DFL techniques, the RTI method makes use of the changes of received signal strength (RSS) measured on links of the network to estimate the radio frequency (RF) attenuation field and forms an image of the changed field. This image is then used to infer the locations of targets within the deployed network. However, there still lacks an efficient scheme which can achieve robust location estimation performance with low computational cost. To solve this problem, we propose a lightweight robust RTI approach in this paper. The proposed method not only can reduce the algorithm's storage and computational resource requirements, but also exploits the location information of wireless measurement nodes to improve the accuracy of the localization result, which ensures its robust performance. The effectiveness and robustness of the proposed scheme are demonstrated by experimental results where the proposed algorithm yields substantial improvement for localization performance and complexity.

1. Gezici, S., "A survey on wireless position estimation," Wireless Personal Communications, Vol. 44, No. 3, 263-282, 2008.
doi:10.1007/s11277-007-9375-z Google Scholar

2. Mitilineos, S. A. and S. C. A. Thomopoulos, "Positioning accuracy enhancement using error modeling via a polynomial approximation approach," Progress In Electromagnetics Research, Vol. 102, 49-64, 2010.
doi:10.2528/PIER10010102 Google Scholar

3. He, S. N. and S. H. G. Chan, "Wi-Fi fingerprint-based indoor positioning: Recent advances and comparisons," IEEE Trans. Surv. Tutor., Vol. 18, No. 1, 466-490, 2016.
doi:10.1109/COMST.2015.2464084 Google Scholar

4. Guvenc, I. and C. C. Chong, "A survey on TOA based wireless localization and NLOS mitigation techniques," IEEE Commun. Surveys & Tutorials, Vol. 11, 107-124, 2009.
doi:10.1109/SURV.2009.090308 Google Scholar

5. Liu, H., H. Darabi, H. Banerjee, and J. Liu, "Survey of wireless indoor positioning techniques and systems," IEEE Trans. Systems, Man, and Cybernetics - Part C, Vol. 37, No. 6, 1067-1080, 2007.
doi:10.1109/TSMCC.2007.905750 Google Scholar

6. Patwari, N. and J. Wilson, "RF sensor networks for device-free localization: Measurements, models, and algorithms," Proc. of the IEEE, Vol. 98, No. 11, 1961-1973, 2010.
doi:10.1109/JPROC.2010.2052010 Google Scholar

7. Youssef, M., M. Mah, and A. Agrawala, "Challenges: Device-free passive localization for wireless environments," 13th ACM MobiCom, 222-229, 2007. Google Scholar

8. Seifeldin, M., A. Saeed, A. Kosba, A. El-keyi, and M. Youssef, "Nuzzer: A large-scale device-free passive localization system for wireless environments," IEEE Trans. Mob. Comput., Vol. 12, No. 7, 1321-1334, 2013.
doi:10.1109/TMC.2012.106 Google Scholar

9. Saeed, A., A. Kosba, and M. Youssef, "Ichnaea: A low-overhead robust WLAN device-free passive localization system," IEEE J. Sel. Topics Signal Process., Vol. 8, No. 1, 5-15, 2014.
doi:10.1109/JSTSP.2013.2287480 Google Scholar

10. Sabek, I., M. Youssef, and A. V. Vasilakos, "ACE: An accurate and efficient multi-entity device-Free WLAN localization system," IEEE Trans. Mob. Comput., Vol. 14, No. 2, 261-273, 2015.
doi:10.1109/TMC.2014.2320265 Google Scholar

11. Xiao, J., K. Wu, Y. Yi, L. Wang, and L. M. Ni, "Pilot: Passive device-free indoor localization using channel state information," Proc. 33th Int. Conf. Distrib. Comput. Syst., 236-245, 2013. Google Scholar

12. Mager, B., P. Lundrigan, and N. Patwari, "Fingerprint-based device-free localization performance in changing environments," IEEE J. Sel. Areas Commun., Vol. 33, No. 11, 2429-2438, 2015.
doi:10.1109/JSAC.2015.2430515 Google Scholar

13. Zhang, D., J. Ma, Q. Chen, and L. Ni, "An RF-based system for tracking transceiver-free objects," Proc. Fifth Annual IEEE International Conference on Pervasive Computing and Communications, 135-144, 2007. Google Scholar

14. Zhang, D., K. Lu, R. Mao, R. Y. Feng, Y. Liu, Z. Ming, and L. Ni, "Fine-grained localization for multiple transceiver-free objects by using RF-based technologies," IEEE Trans. Parallel Distrib. Syst., Vol. 25, No. 6, 1464-1475, 2014.
doi:10.1109/TPDS.2013.243 Google Scholar

15. Talampas, M. C. R. and K. S. Low, "A geometric filter algorithm for robust device-free localization in wireless networks," IEEE Trans. Ind. Informat., Vol. 12, No. 5, 1670-1678, 2016.
doi:10.1109/TII.2015.2433211 Google Scholar

16. Wilson, J. and N. Patwari, "Radio tomographic imaging with wireless networks," IEEE Trans. Mob. Comput., Vol. 9, No. 5, 621-632, 2010.
doi:10.1109/TMC.2009.174 Google Scholar

17. Kaltiokallio, O., M. Bocca, and N. Patwari, "Enhancing the accuracy of radio tomographic imaging using channel diversity," Proc. 9th IEEE Int. Conf. MASS, 254-262, 2012. Google Scholar

18. Bocca, M., A. Luong, N. Patwari, and T. Schmid, "Dial it in: Rotating RF sensors to enhance radio tomography,", arXiv, 2013, [Online], Available: http://arxiv.org/abs/1312.5480. Google Scholar

19. Wang, J., Q. Gao, H.Wang, P. Cheng, and K. Xin, "Device-free localization with multi-dimensional wireless link information," IEEE Trans. Veh. Technol., Vol. 64, No. 1, 356-366, 2015.
doi:10.1109/TVT.2014.2318084 Google Scholar

20. Yang, Z. Y., K. D. Huang, X. M. Guo, and G. L.Wang, "A real-time device-free localization system using correlated RSS measurements," EURASIP J. Wireless Commu. Netw., Vol. 2013, No. 186, 1-12, 2013. Google Scholar

21. Wang, J., Q. Gao, X. Zhang, and H. Wang, "Device-free localization with wireless networks based on compressing sensing," IET Commun., Vol. 6, No. 15, 2395-2403, 2012.
doi:10.1049/iet-com.2011.0603 Google Scholar

22. Kanso, M. A. and M. G. Rabbat, "Compressed RF tomography for wireless sensor networks: centralized and decentralized approaches," Proc. 5th DCOSS, 173-186, 2009. Google Scholar

23. Ke, W., G. Liu, and T. Fu, "Robust sparsity-based device-free passive localization in wireless networks," Progress In Electromagnetics Research C, Vol. 46, 63-73, 2014.
doi:10.2528/PIERC13101301 Google Scholar

24. Hamilton, B. R., X. L. Ma, R. J. Baxley, and S. M. Matechik, "Propagation modeling for radio frequency tomography in wireless networks," IEEE J. Sel. Topics Signal Process., Vol. 8, No. 1, 43-54, 2014.
doi:10.1109/JSTSP.2013.2287471 Google Scholar

25. Guo, Y., K. Huang, N. Jiang, X. Guo, and G. Wang, "An exponential-Rayleigh model for RSS-based device-free localization and tracking," IEEE Trans. Mob. Comput., Vol. 14, No. 3, 484-494, 2015.
doi:10.1109/TMC.2014.2329007 Google Scholar

26. Wang, Z. H., H. Liu, S. X. Xu, X. Y. Bu, and J. P. An, "A diffraction measurement model and particle filter tracking method for RSS-based DFL," IEEE J. Sel. Areas Commun., Vol. 33, No. 11, 2391-2403, 2015.
doi:10.1109/JSAC.2015.2430517 Google Scholar

27. Wang, J., Q. H. Gao, M. Pan, X. Zhang, Y. Yu, and H. Y. Wang, "Toward accurate device-free wireless localization with a saddle surface model," IEEE Trans. Veh. Technol., Vol. 65, No. 8, 6665-6677, 2016.
doi:10.1109/TVT.2015.2476495 Google Scholar

Dr. Xiao Cao

Dr. Hongchun Yao

Dr. Yixian Ge

Dr. Wei Ke