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2019-06-18
Enhanced Radio Tomographic Imaging Method for Device-Free Localization Using a Gradual-Changing Weight Model
By
Progress In Electromagnetics Research M, Vol. 82, 39-48, 2019
Abstract
Radio tomographic imaging (RTI) is a main method in device-free localization (DFL) that can locate a target by analyzing its shadowing effect on wireless links, while removing the requirement of equipping the target with a device. The accuracy of RTI method closely depends on the accuracy of shadowing weight model, which represents the relationship between the shadowing effect of the target on wireless links and target location. However, most existing models have not been accurate enough for many applications since they cannot explain some phenomena observed in DFL practices. To overcome the shortcoming of the existing weight model, this paper proposes a gradual-changing weight model to enhance the imaging quality of RTI. Meanwhile, a foreground target detection algorithm based on the shape feature of the target image is proposed to reduce the negative impact of background noises and pseudo-targets, thereby further enhancing the localization accuracy. The indoor and outdoor experimental results highlight the advantages of using the proposed method in improving the imaging quality and the positioning accuracy.
Citation
Wei Ke, Haoran Zuo, Mengling Chen, and Yanli Wang, "Enhanced Radio Tomographic Imaging Method for Device-Free Localization Using a Gradual-Changing Weight Model," Progress In Electromagnetics Research M, Vol. 82, 39-48, 2019.
doi:10.2528/PIERM19041603
References

1. 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

2. Shukri, S. and L. M. Kamarudin, "Device free localization technology for human detection and counting with RF sensor networks: A review," J. Netw. and Computer Appli., Vol. 97, No. 1, 157-174, 2017.
doi:10.1016/j.jnca.2017.08.014

3. 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

4. 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

5. 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

6. 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

7. 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

7. Zhao, Y. and N. Patwari, "Robust estimators for variance-based device-free localization and tracking," IEEE Trans. Mob. Comput., Vol. 14, No. 10, 2116-2129, 2015.
doi:10.1109/TMC.2014.2385710

8. Zhao, Y. and N. Patwari, "Histogram distance-based radio tomographic localization," Proc. 11th Int. Conf. IPSN, 129-130, 2012.

9. 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.

10. Ke, W., Y. Yuan, X. Zhang, and J. Shao, "Device-free electromagnetic passive localization with frequency diversity," Progress In Electromagnetics Research M, Vol. 47, 129-139, 2016.
doi:10.2528/PIERM15102502

11. Wilson, J. and N. Patwari, "A fade-level skew-laplace signal strength model for device-free localization with wireless networks," IEEE Trans. Mob. Comput., Vol. 11, No. 6, 947-958, 2012.
doi:10.1109/TMC.2011.102

12. 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.

13. Ke, W. and T. T. Wang, "Enhanced CS-based device-free localization with RF sensor networks," IEEE Commun. Lett., Vol. 22, No. 12, 2503-2506, 2018.
doi:10.1109/LCOMM.2018.2876896

14. 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

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

16. 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

17. 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

18. 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

19. 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

20. Rappaport, T. S., Wireless Communication: Principles and Practice, Prentice-Hall, Englewood Cliffs, NJ, 1999.

21. Athanasiadou, G. E., "Incorporating the fresnel zone theory in ray tracing for propagation modelling of fixed wireless access channels," Proc. 18th IEEE Int. Conf. PIMRC, 1-5, 2007.