Progress In Electromagnetics Research
ISSN: 1070-4698, E-ISSN: 1559-8985
Home | Search | Notification | Authors | Submission | PIERS Home | EM Academy
Home > Vol. 95 > pp. 267-282


By H.-Q. Liu, H.-C. So, K. W. K. Lui, and F. K. W. Chan

Full Article PDF (315 KB)

This paper addresses the sensor selection problem which is a very important issue where many sensors are available to track a target. In this problem, we need to select an appropriate group of sensors at each time to perform tracking in a wireless sensor network (WSN). As the theoretical tracking performance is bounded by posterior Cramer-Rao lower bound (PCRLB), it is used as a criterion to select sensors. Based on the PCRLB, sensor selection algorithms with and without sensing range constraint are developed. Without sensing range limit, exhaustive enumeration is first adopted to search all possible combinations for sensor selection. To reduce complexity of enumeration, second, we restrict the selected sensors to be within a fixed area in the WSN. With sensing range constraint, a circle will be drawn with the help of communication range for sensor selection. In a similar manner, two approaches, namely, selecting all sensors inside the circle or using enumeration to select sensors within the circle are presented. The effectiveness of the proposed methods is validated by computer simulation results in target tracking for WSNs.

H.-Q. Liu, 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.

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, 2005.

2. Gezici, S., Z. Tian, G. B. Giannakis, H. Kobayashi, A. F. Molisch, H. V. Poor, and Z. Sahinoglu, "Localization via ultra-wideband radios: A look at positioning aspects for future sensor networks," IEEE Signal Processing Magazine, Vol. 22, No. 4, 70-84, 2005.

3. Zhao, F., J. Shin, and J. Reich, "Information-driven dynamic sensor collaboration," IEEE Signal Processing Magazine, Vol. 19, No. 2, 61-72, 2002.

4. 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, 2004.

5. Isler, V. and R. Bajcsy, "The sensor selection problem for bounded uncertainty sensing models," IEEE Trans. Automation Science and Engineering, Vol. 3, No. 4, 372-380, 2006.

6. Tharmarasa, R., T. Kirubarajan, and M. L. Hernandez, "Large-scale optimal sensor array management for multitarget tracking," IEEE Trans. Systems, Man, and Cybernetics --- Part C: Applications and Reviews, Vol. 37, No. 5, 803-814, 2007.

7. Chhetri, A. S., D. Morrell, and A. Papandreou-Suppappola, "The use of particle filtering with the unscented transform to schedule sensors," Proc. ICASSP-04, Vol. 2, 301-304, Montreal, QC, Canada, 2004.

8. Thatte, G. and U. Mitra, "Sensor selection and power allocation for distributed estimation in sensor networks: Beyond the star topology," IEEE Trans. Signal Processing, Vol. 56, No. 7, 2649-2661, 2008.

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

10. Vander Merwe, R., A. Doucet, N. de Freitas, and E. Wan, "The unscented particle filter," Advances in Neural Information Processing Systems, Vol. 13, 2000.

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

12. Hue, C., J. Cadre, and P. Perez, "Sequential Monte Carlo methods for multiple target tracking and data fusion," IEEE Trans. Signal Processing, Vol. 50, No. 2, 309-325, 2002.

13. Bergman, N., Recursive Bayesian Estimation: Navigation and Tracking Applications, Ph.D. Thesis, Linkoping Universit, Sweden, 1999.

14. Tichavsky, P., C. H. Muravchik, and A. Nehorai, "Posterior Cramer-Rao bounds for discrete-time nonlinear filtering," IEEE Trans. Signal Processing, Vol. 46, No. 5, 1386-1396, 1998.

15. Robert, C. P. and G. Casella, Monte Carlo Statistical Methods, Springer, New York, 1999.

16. 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.

© Copyright 2014 EMW Publishing. All Rights Reserved