Decentralized Fusion Filtering with Different Sensor Memories in Dynamics System with Uncertainties

Abstract:

This paper is concerned with the decentralized sliding window Kalman filter for multisensor discrete-time dynamic linear systems having different sensor memories. The proposed filtering is presented that combines the Kalman filter and sliding window strategy. A decentralized fusion with the weighted sum structure is applied to the local sliding window Kalman filters (LSWKFs) having different sensor memory size. The proposed decentralized algorithm has a parallel structure and allows parallel processing of observations, thereby it more reliable than the centralized version if some sensors become faulty. Moreover, the choice of sliding window strategy makes the proposed algorithm robust against dynamic model uncertainties. The derivation of the error cross-covariances between the LSWKFs is the key idea of this paper. The application of the proposed decentralized filer to linear discrete-time dynamic systems within a multisensor environment demonstrates its high accuracy and computational efficiency.

• Periodical: Applied Mechanics and Materials (Volumes 446-447)
• Pages: 688-697
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.446-447.688
• Online since: November 2013
• Authors: Il Young Song, Seongdo Huh, Vladimir Shin
• Keywords: Decentralized Fusion, Fusion Formula, Kalman Filter (KF), Multisensor, Sliding Window

References

[1] Y. Bar-Shalom, and X. R. Li: Multitarget-multisensor tracking: Principles and Techniques (YBS Publishing, Storrs 1995).

[2] Y. M. Zhu: Multisensor Decision and Estimation Fusion (Kluwer Academic, Boston 2003).

[3] Y. Bar-Shalom, and L. Campo: IEEE Trans. Aerospace and Electronic Systems, Vol. 22-11 (1986), pp.803-805.

[4] H. R. Hashemipour, S. Roy, and A. J. Laub: IEEE Trans. Automatic Control, Vol. 33-1 (1988), pp.88-94.

[5] T. M. Berg, and H. F. Durrant-Whyte: In: Proc. American Control Conf., Maryland (1994), pp.2273-2274.

[6] Y. Zhu, Z. You, J. Zhao, K. Zhang, and X. R. Li: Automatica, Vol. 37 (2001), pp.148-1393.

[7] X. Li, R. Zhu, Y. M. Wang, J. Han, and C. Z.: IEEE Trans. Inf. Theory, Vol. 49-9 (2004), pp.2192-2208.

[8] J. Zhou, Y. Zhu, Z. You, and E. Song: IEEE Trans. Syst., Man, Cybern., Vol. 36-5 (2006), pp.1000-1009.

[9] W. H. Kwon, K. S. Lee, and O. K. Kwon: IEEE Trans. on Aerospace and Electronic Systems, Vol. 26 (1990), pp.1011-1021.

[10] W. H. Kwon, P. S. Kim, and P. Park: IEEE Trans. on Automatic Control, Vol. 44 (1999), pp.1787-1791.

[11] D. Y. Kim and V. Shin: Proc. 6th WSEAS Inter. Conf. on Signal Processing, Dallas, Texas, USA (2007), pp.112-116.

[12] D. Y. Kim, and V. Shin: Proc. Intern. Conf. SICE-ICCAS, Busan, Korea (2006), pp.263-265.

[13] I. Y. Song, D. Y. Kim, and V. Shin: Proc. the 10th IASTED Intern. Conf. SIP 2008, Kailua-Kona, HI, USA (2008), pp.238-242.

[14] V. Shin, Y. Lee, and T-S. Choi: Signal Processing, Vol. 86-2 (2006), pp.257-266.

[15] Y. Bar-Shalom, X. R. Li, and T. Kirubarajan: Estimation with Applications to Tracking and Navigation (John Wiley & Sons, New York 2001).