Quantized Kalman Filter for Sensor Networks with Random Packet Dropouts

Abstract:

Based on the projection theory and the uniform quantization method, a quantized Kalman filter is presented for sensor networks with random packet dropouts from sensors to the fusion center (filter). The bandwidths are scheduled by the optimality index that energy consumption is minimized under a given performance constraint. Compared with the filter without quantization, the quantized filter given can reduce the energy consumption and has an effective tracking performance. A simulation example demonstrates the effectiveness of the proposed algorithm.

• Periodical: Advanced Materials Research (Volumes 219-220)
• Pages: 1040-1044
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.219-220.1040
• Online since: March 2011
• Authors: Na Liu, Jing Ma, Shu Li Sun
• Keywords: Kalman Filter (KF), Quantization, Random Packet Dropout, Sensor Network

References

[1] R. M. Gray, and D. L. Neuhoff, Quantization, IEEE Trans. Information Theory, vol. 44, no. 6, Oct. 1998, pp.2325-2383.

DOI: 10.1109/18.720541

[2] M. Sahebsara, T. Chen, and S. L. Shah, Optimal H2 filtering in networked control systems with multiple packet dropout, IEEE Trans. on Automatic Control, vol. 52, no. 8, pp.1508-1513, (2007).

DOI: 10.1109/tac.2007.902766

[3] S. L. Sun, L. H. Xie, W. D. Xiao, and Y. C. Soh, Optimal Linear Estimation for Systems with Multiple Packet Dropouts, Automatica, vol. 44, no. 5: pp.1333-1342, (2008).

DOI: 10.1016/j.automatica.2007.09.023

[4] S. L. Sun, and W. D. Xiao, Distributed Weighted Fusion Estimators with Random Delays and Packet Dropping, Circuits Systems Signal Processing, vol. 26, No. 4, 2007. pp.591-605.

DOI: 10.1007/s00034-007-4009-5

[5] S. L. Sun, Optimal Estimators for Systems With Finite Consecutive Packet Dropouts, IEEE Signal Processing Letters, vol. 16, no. 7, pp.557-560, (2009).

DOI: 10.1109/lsp.2009.2018909

[6] J. J. Xiao, S. G. Cui, Z. Q. Luo, and A. J. Goldsmith, Power scheduling of universal decentralized estimation in sensor networks, IEEE Trans. Signal Processing, vol. 54, no. 2, pp.413-422, (2006).

DOI: 10.1109/tsp.2005.861898

[7] Z. Q. Luo, Universal Decentralized Estimation in a Bandwidth Constrained Sensor Network, IEEE Trans. Information Theory, vol. 51, no. 6, pp.2210-2219, June (2005).

DOI: 10.1109/tit.2005.847692

[8] A. Ribeiro, G. B. Giannakis, and S. I. Roumeliotis, SOI-KF: distributed Kalman filtering with low-cost communications using the sign of innovations, IEEE Trans. Signal Processing, vol. 54, no. 1, pp.2617-2624, (2006).

DOI: 10.1109/tsp.2006.882059

[9] S. L. Sun, J. Y. Lin, L. H. Xie, and W. D. Xiao, Quantized Kalman Filtering, IEEE 22nd International Symposium on Intelligent Control, USA: IEEE, pp.7-12, (2007).

DOI: 10.1109/isic.2007.4450852

[10] M. Y. Fu, and C. E. d. Souza, State estimation for linear discrete-time systems using quantized measurements, Automatica, vol. 45, pp.2937-2945, (2009).

DOI: 10.1016/j.automatica.2009.09.033

[11] L. H. Xie, and L. Shi, State Estimation over Unreliable Network, Proceedings of the 7th Asian Control Conference, Hong Kong, China, pp.453-458, August 27-29, (2009).

[12] B. D. O. Anderson, and J. B. Moore, Optimal Filtering. Englewood Cliffs, New Jersey: Prentice-Hall, (1979).