Progress In Electromagnetics Research
ISSN: 1070-4698, E-ISSN: 1559-8985
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By H.-Q. Liu and H.-C. So

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Tracking a target is a fundamental and crucial problem in wireless sensor networks. It is well known that non-line-of-sight (NLOS) propagation will significantly degrade the tracking accuracy if its effects are ignored. In this paper, a line-of-sight (LOS) identification approach for range-based tracking systems is developed to discard the NLOS measurements. Based on Lp-norm LOS identification strategy, a novel target tracking method is devised with the use of cost-reference particle filter, which does not require the knowledge of the measurement noise distribution. Computer simulations are included to verify the effectiveness of the proposed approach under different noise distributions.

H.-Q. Liu and H.-C. So, "Target tracking with line-of-sight identification in sensor networks under unknown measurement noises," Progress In Electromagnetics Research, Vol. 97, 373-389, 2009.

1. Patwari, N., J. N. Ash, S. Kyperountas, A. O. Hero III, R. L. Moses, and N. S. Correal, "Locating the nodes --- Cooperative localization in wireless sensor networks," IEEE Signal Processing Magazine, Vol. 22, No. 4, 54-69, Jul. 2005.

2. Guo, D. and X. Wang, "Dynamic sensor collaboration via sequential Monte Carlo," IEEE Journal on Selected Areas in Communications, Vol. 22, No. 6, 1037-1047, Aug. 2004.

3. Cong, L. and W. Zhuang, "Non-line-of-sight error mitigation in mobile location," IEEE Trans. on Wireless Communications, Vol. 4, No. 2, 560-573, Mar. 2005.

4. Gezici, S., H. Kobayashi, and H. V. Poor, "Non parametric nonline of sight identification," Proc. IEEE 58th Veh. Technol. Conf., Vol. 4, No. 6-9, 2544-2548, Oct. 2003.

5. Wylie, M. P. and J. Holtzman, "The non-line of sight problem in mobile location estimation," Proc. IEEE Int. Conf. Universal Personal Commun., Vol. 2, No. 29, 827-831, Oct. 1996.

6. Ristic, B., A. Arulampalam, and N. Gordon, Beyond the Kalman Filter-particle Filters for Tracking Applications,, Artech House, Boston, 2004.

7. Arulampalam, M. S., S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking," IEEE Trans. on Singal Processing, Vol. 50, No. 2, 174-188, Feb. 2002.

8. Miguez, J., M. F. Bugallo, and P. M. Djuric, "A new class of particle ¯lters for random dynamical systems with unknown statistics," EURASIP Journal on Applied Signal Processing, Vol. 2004, No. 15, 2278-2294, 2004.

9. Lu, T., M. F. Bugallo, and P. M. Djurie, "RLS-assisted cost reference particle filtering," Proc. ICASSP-08, 3421-3424, Las Vegas, USA, Mar. 2008.

10. Vander Merwe unscented particle filter, R., A. Doucet, N. De Freitas, and E. Wan, The unscented particle filter, Technical Report, Cambridge University Engineering Department, Dec. 2000.

11. Chen, L. and L. Wu, "\Mobile localization with NLOS mitigation using improved Rao-Blackwellized particle ¯ltering algorithm," Proc. 13th IEEE Int. Symposium on Consumer Electronics, 174-178, Kyoto, Japan, May 2009.

12. Caffery, J. J., Wireless location in CDMA Cellular Radio Systems, Kluwer Academic, Boston, 2000.

13. Pitas, I. and A. N. Venetsanopoulos, Nonlinear Digital Filters: Principles and Applications, Kluwer Academic Publishers, Norwell, MA, 1990.

14. Kay, S. M., Fundamentals of Statistical Signal Processing: Estimation Theory, Prentice Hall, 1993.

15. Rice, J. A., Mathematical Statistics and Data Analysis, , 2nd Ed., Duxbury Press, Belmont, Calif., 1995.

16. Bhatia, V. and B. Mulgrew, "Non-parametric likelihood based channel estimator for Gaussian mixture noise," Signal Processing, Vol. 87, No. 11, 2569-2586, Nov. 2007.

17. Stein, D. W. J., "Detection of random signals in Gaussian mixture noise," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 41, No. 6, 1788-1801, Nov. 1995.

18. Nikias, C. L. and M. Shao, Signal Processing with Alpha-stable Distributions and Applications, A Wiley-Interscience, 1995.

19. Kuruoglu, E. E., Signal Pprocessing in α-Stable Noise Environments: A Least lp-Norm Approach, Ph.D. dissertation, Department of Engineering, University of Cambridge, Nov. 1998.

20. Weron, R., "On the Chambers-Mallows-Stuck method for simulating skewed stable random variables," Statistics & Probability Letters, Vol. 28, No. 2, 165-171, Jun. 1996.

21. Liu, H. Q., H. C. So, K. W. K. Lui, and F. K. W. Chan, "Sensor selection for target tracking in sensor networks," Progress In Electromagnetics Research, Vol. 95, 267-282, 2009.

22. Chen, J. F., Z. G. Shi, S. H. Hong, and K. S. Chen, "Grey prediction based particle filter for maneuvering target tracking," Progress In Electromagnetics Research, Vol. 93, 237-254, 2009.

23. Khodier, M. M. and M. Al-Aqeel, "Linear and circular array optimization: A study using particle swarm intelligence," Progress In Electromagnetics Research B, Vol. 15, 347-373, 2009.

24. Lim, T. S., V. C. Koo, H.-T. Ewe, and H.-T. Chuah, "A SAR autofocus algorithm based on particle swarm optimization," Progress In Electromagnetics Research B, Vol. 1, 159-176, 2008.

25. Liu, T. H. and J. M. Mendel, "A subspace-based direction finding algorithm using fractional lower order statistics," IEEE Trans. on Singal Processing, Vol. 49, No. 8, 1605-1613, Aug. 2001.

26. Figueiredo, M. A. T. and A. K. Jain, "Unsupervised learning of finite mixture models," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 24, No. 3, 381-396, Mar. 2002.

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