DISTRIBUTED KALMAN FILTER-BASED TARGET TRACKING IN WIRELESS SENSOR NETWORKS

Phuong Pham, Sesh Commuri

2010

Abstract

The tracking of mobile targets using Distributed Kalman Filters in a Wireless Sensor Network (WSN) is addressed in this paper. In contrast to the Kalman Filter implementations reported in the literature, our approach has the Kalman Filter running on only one network node at any given time. The knowledge learned by this node, i.e. the system state and the covariance matrix, is passed on to the subsequent node running the filter. Since a finite subset of the sensor nodes is active at any given time, target tracking can be accomplished using lower power compared to centralized implementations of the Kalman Filter. Numerical simulations demonstrate that the proposed algorithm is robust to measurement noise and changes in the velocity of the target. The results in this paper show that the proposed technique for target tracking will result in significant savings in power consumption and will extend the useful life of the WSN.

References

  1. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor network, IEEE Communications Magazine (Vol. 40 pp. 102-114).
  2. Al-Karaki, J. N., & Kamal, A. E. (2004). Routing techniques in wireless sensor networks: a survey. Wireless Communications, IEEE, 11(6), 6-28.
  3. Alriksson, P., & Rantzer, A. (2007, December 12-14 ). Experimental Evaluation of a Distributed Kalman Filter Algorithm. Paper presented at the 2007 46th IEEE Conference on Decision and Control, New Orleans, LA
  4. Cardei, M., Thai, M. T., Li, Y., & Wu, W. (2005, March 13-17). Energy-efficient target coverage in wireless sensor networks. Paper presented at the INFOCOM 2005. 24th Annual Joint Conference of the IEEE Computer and Communications Societies, Miami, Florida.
  5. Cattivelli, F. S., Lopes, C. G., & Sayed, A. H. (2008, June). Diffusion strategies for distributed kalman filtering: Formulation and performance analysis. Paper presented at the Proc. 2008 IAPR Workshop on Cognitive Information Processing, Santorini, Greece.
  6. Chen, M., Gonzalez, S., & Leung, V. C. M. (2007). Applications and design issues for mobile agents in wireless sensor networks. IEEE Wireless Communications, 14(6), 20-26.
  7. Chiang, C., Wu, H., Liu, W., & Gerla, M. (1997, April). Routing In Clustered Multihop, Mobile Wireless Networks With Fading Channel. Paper presented at the In Proc. IEEE SICON'97.
  8. Hashemipour, H. R., Roy, S., & Laub, A. J. (1998). Decentralized structures for parallel Kalman filtering. IEEE Transaction on Automatic Control, 33(1), 88-94.
  9. Intanagonwiwat, C., Govindan, R., & Estrin, D. (2000, August). Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks. Paper presented at the In Proceedings of the Sixth Annual International Conference on Mobile Computing and Networking (MobiCOM 7800).
  10. Kim, J.-H., West, M., Scholte, E., & Narayanan, S. (2008, June 11-13). Multiscale consensus for decentralized estimation and its application to building systems. Paper presented at the 2008 American Control Conference, Seattle, WA
  11. Mutambara, G. O. (1998). Decentralized estimation and control for multisensor systems: CRC Press.
  12. Olfati-Saber, R. (2007, December 12-14). Distributed Kalman filtering for sensor networks. Paper presented at the 2007 46th IEEE Conference on Decision and Control, New Orleans, LA
  13. Olfati-Saber, R., & Shamma, J. S. (2005, December 12- 15). Consensus Filters for Sensor Networks and Distributed Sensor Fusion. Paper presented at the 44th IEEE Conference on Decision and Control, CDCECC'05.
  14. Rao, B. S., & Durrant-Whyte, H. F. (1991). Fully decentralized algorithm for multisensor Kalman filtering. IEE Proceedings-D Control Theory & Application, 138(5), 413 - 420.
  15. Uhlmann, J. K. (1996). General data fusion for estimates with unknown cross covariances. Proceedings of SPIE, 2755, 536-547.
  16. Watfa, M. K., & Commuri, S. (2006a, August 14). The 3- Dimensional Wireless Sensor Network Coverage Problem. Paper presented at the 2006 IEEE International Conference on Networking, Sensing and Control. ICNSC 7806, Ft. Lauderdale, FL.
  17. Watfa, M. K., & Commuri, S. (2006b). Optimal sensor placement for Border Perambulation. Paper presented at the 2006 IEEE International Conference on Control Applications, Munich, Germany.
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Paper Citation


in Harvard Style

Pham P. and Commuri S. (2010). DISTRIBUTED KALMAN FILTER-BASED TARGET TRACKING IN WIRELESS SENSOR NETWORKS . In Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO, ISBN 978-989-8425-02-7, pages 54-61. DOI: 10.5220/0002952900540061


in Bibtex Style

@conference{icinco10,
author={Phuong Pham and Sesh Commuri},
title={DISTRIBUTED KALMAN FILTER-BASED TARGET TRACKING IN WIRELESS SENSOR NETWORKS},
booktitle={Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,},
year={2010},
pages={54-61},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002952900540061},
isbn={978-989-8425-02-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,
TI - DISTRIBUTED KALMAN FILTER-BASED TARGET TRACKING IN WIRELESS SENSOR NETWORKS
SN - 978-989-8425-02-7
AU - Pham P.
AU - Commuri S.
PY - 2010
SP - 54
EP - 61
DO - 10.5220/0002952900540061