volume | PIER Journals

Abstract

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.

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.
doi:10.1109/MSP.2005.1458287 Google Scholar

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.
doi:10.1109/MSP.2005.1458289 Google Scholar

3. Zhao, F., J. Shin, and J. Reich, "Information-driven dynamic sensor collaboration," IEEE Signal Processing Magazine, Vol. 19, No. 2, 61-72, 2002.
doi:10.1109/79.985685 Google Scholar

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.
doi:10.1109/JSAC.2004.830897 Google Scholar

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.
doi:10.1109/TASE.2006.876615 Google Scholar

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.
doi:10.1109/TSMCC.2007.901003 Google Scholar

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. Google Scholar

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.
doi:10.1109/TSP.2008.917038 Google Scholar

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

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.
doi:10.1109/78.978374 Google Scholar

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.
doi:10.1109/78.978386 Google Scholar

13. Bergman, N., Recursive Bayesian Estimation: Navigation and Tracking Applications, Ph.D. Thesis, Linkoping Universit, 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.
doi:10.1109/78.668800 Google Scholar

15. Robert, C. P. and G. Casella, Monte Carlo Statistical Methods, Springer, 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.
doi:10.2528/PIER09042204 Google Scholar

Dr. Hong-Qing Liu

Dr. Hing-Cheung So

Dr. Kenneth Wing Kin Lui

Dr. Frankie Kit Wing Chan