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Progress In Electromagnetics Research C
ISSN: 1937-8718
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ONLINE-CALIBRATED CS-BASED INDOOR LOCALIZATION OVER IEEE 802.11 WIRELESS INFRASTRUCTURE

By W. Ke, J. Jin, H. Xu, K. Yu, and J. Shao

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Abstract:
Recent technological achievements have made it low cost to realize indoor localization using the received signal strength (RSS) information from Wi-Fi signals. However, the current RSS-based indoor localization techniques have two major challenges: one is that the RSS signal is quite sensitive to channel conditions, and the other is that sufficient number of access points (APs) is needed to provide enough RSS measurements for guaranteeing good performance. To solve these problems, this paper proposes an adaptive compressive sensing (CS) based indoor localization method based on the IEEE 802.11 Wi-Fi standard. The novel feature of this method is to dynamically adjust both the dictionary and the sparse solution using an online dictionary learning (DL) technology so that the location solution can better match the real-time RSS scenario. Meanwhile, an improved approximate l0 norm minimization algorithm is presented to enhance sparse recovery speed and reduce the number of APs required by indoor localization systems. The effectiveness of the proposed scheme is demonstrated by experimental results where the proposed algorithm yields substantial improvement for localization performance and reduces computation complexity.

Citation:
W. Ke, J. Jin, H. Xu, K. Yu, and J. Shao, "Online-Calibrated CS-Based Indoor Localization Over IEEE 802.11 Wireless Infrastructure," Progress In Electromagnetics Research C, Vol. 70, 73-81, 2016.
doi:10.2528/PIERC16101602

References:
1. Gonzalo, S. G., A. L. Jose, J. B. David, and L. R. Gustavo, "Challenges in indoor global navigation satellite systems," IEEE Signal Process. Mag., Vol. 29, No. 2, 108-131, 2012.
doi:10.1109/MSP.2011.943410

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

3. Dianu, M. D., J. Riihijarvi, and M. Petrova, "Measurement-based study of the performance of IEEE 802.11ac in an indoor environment," Proc. IEEE International Conference on Communications, 5771-5776, Sydney, Australia, Jun. 2014.

4. Kaemarungsi, K. and P. Krishnamurthy, "Properties of indoor received signal strength for Wi-Fi location fingerprinting," Proc. IEEE International Conference on Mobile and Ubiquitous System, 14-23, Boston, USA, Aug. 2004.

5. Golmie, N., "Interference in the 2.4GHz band," Proc. International Conference on Applications and Services in Wireless Networks, 2540-2545, Helsinki, Finland, Jue. 2001.

6. 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

7. Cevher, V., M. F. Duarte, and R. G. Baraniuk, "Distributed target localization via spatial sparsity," Proc. 16th European Signal Processing Conference, 15-19, Lausanne, Switzerland, Aug. 2008.

8. Feng, C., W. S. A. Au, S. Valaee, and Z. Tan, "Received signal strength based indoor positioning using compressive sensing," IEEE Trans. Mobile Computing, Vol. 11, No. 12, 1983-1993, 2012.
doi:10.1109/TMC.2011.216

9. Zhang, L. and Z. Tan, "Study on compressive sensing in the application of wireless localization," J. Internet Technol., Vol. 11, No. 1, 129-134, 2010.

10. Zhang, B., X. Cheng, N. Zhang, Y. Cui, Y. Li, and Q. Liang, "Sparse target counting and localization in sensor networks based on compressive sensing," Proc. IEEE INFOCOM, 2255-2263, Shanghai, China, Apr. 2011.

11. Feng, C., W. S. A. Au, S. Valaee, and Z. Tan, "Orientation-aware indoor localization using affinity propagation and compressive sensing," Proc. IEEE International Workshop on Computational Advances in Multi-sensor Adaptive Processing, 261-264, Aruba, Netherlands, Dec. 2009.

12. Ke, W. and L.Wu, "Indoor localization in the presence of RSS variations via sparse solution finding and dictionary learning," Progress In Electromagnetics Research B, Vol. 45, 353-368, 2012.
doi:10.2528/PIERB12091405

13. Chang, N., R. Rashidzadeh, and M. Ahmadi, "Robust indoor positioning using differential Wi-Fi access points," IEEE Trans. Consumer Electron., Vol. 56, No. 3, 1860-1867, Aug. 2010.
doi:10.1109/TCE.2010.5606338

14. Lim, H., L. C. Kung, and J. C. Hou, "Zero-configuration indoor localization over IEEE 802.11 wireless infrastructure," Wireless Netw, Vol. 16, No. 2, 405-420, 2010.
doi:10.1007/s11276-008-0140-3

15. Honkavirta, V., T. Perala, S. A. Loytty, and R. Piche, "A comparative survey of Wi-Fi location fingerprinting methods," Proc. 6th Workshop on Positioning, Navigation and Communication, 243-251, Hannover, Germany, Mar. 2009.

16. Fang, S. H., Y. T. Hsu, and W. H. Kuo, "Dynamic fingerprinting combination for improved mobile localization," IEEE Trans. Wireless Comm., Vol. 10, No. 12, 4018-4022, Dec. 2011.
doi:10.1109/TWC.2011.101211.101957

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

18. Tosic, I. and P. Frossard, "Dictionary learning," IEEE Signal Process. Mag., Vol. 28, No. 2, 27-38, 2011.
doi:10.1109/MSP.2010.939537

19. Candes, E. J., M. B. Wakin, and S. P. Boyd, "Enhancing sparsity by reweighted l1 minimization," J. Fourier Anal. Applicat., Vol. 14, No. 5–6, 877-905, Jun. 2008.
doi:10.1007/s00041-008-9045-x

20. Mohimani, H., M. B. Zadeh, and C. Jutten, "A fast approach for overcomplete sparse decomposition based on smoothed l0 norm," IEEE Trans. Signal Process., Vol. 57, No. 1, 289-301, 2009.
doi:10.1109/TSP.2008.2007606

21. Antoniou, A. and W.-S. Lu, Practical Optimization: Algorithms and Engineering Applications, Springer, 2006.

22. Rubinstein, R., A. M. Bruckstein, and M. Elad, "Dictionaries for sparse representation modeling," Proc. IEEE, Vol. 98, No. 6, 1045-1057, 2010.
doi:10.1109/JPROC.2010.2040551

23. Mairal, J., F. Bach, J. Ponce, and G. Sapiro, "Online learning for matrix factorization and sparse coding," J. Mach. Learn. Res., Vol. 11, No. 3, 19-60, Jan. 2010.

24. Cheung, K. W., H. C. So, W.-K. Ma, and Y. T. Chan, "A constrained least squares approach to mobile positioning: Algorithms and optimality," EURASIP J. Applied Signal Process., Vol. 2006, 123, 2006.


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