Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33093
Fusion Filters Weighted by Scalars and Matrices for Linear Systems
Authors: Seok Hyoung Lee, Vladimir Shin
Abstract:
An optimal mean-square fusion formulas with scalar and matrix weights are presented. The relationship between them is established. The fusion formulas are compared on the continuous-time filtering problem. The basic differential equation for cross-covariance of the local errors being the key quantity for distributed fusion is derived. It is shown that the fusion filters are effective for multi-sensor systems containing different types of sensors. An example demonstrating the reasonable good accuracy of the proposed filters is given.Keywords: Kalman filtering, fusion formula, multi-sensor, mean-square error.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1079748
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1394References:
[1] D. L. Hall, Mathematical Techniques in Multisensor Data Fusion, Artech House, London, 1992.
[2] Y.Bar-Shalom (Ed.), Multitarget-Multisensor Tracking: Advanced Applications, Artech House, Norwood, MA, 1990.
[3] Y.Bar-Shalom and X. Rong Li, Multitarget-Multisensor Tracking: Principles and Techniques, YBS Publishing, 1995.
[4] J.Manyika and H.Durrant-Whyte, Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach, Ellis Horwood, New York, 1994.
[5] Y.Bar-Shalom and L. Campo, "The effect of the common process noise on the two-sensor fused-track covariance", IEEE Trans. Aerospace and Electronic Systems, 22(11), pp. 803-805, 1986.
[6] Li, X.R., Zhu, Y.M., Wang, J., Han, C.Z., 2004. Optimal linear estimation fusion - Part I: Unified fusion rules, IEEE Trans. Inf. Theory, vol. 49, no. 9, pp. 2192-2208.
[7] V.I. Shin, Y. Lee and T. Choi, Suboptimal Linear Filtering and Generalized Millman-s Formula, Proc. IASTED Inter. Conf. Signal and Image Process, Honolulu, Hawaii, USA, pp. 369-374, 2004.
[8] V.Shin, Y.Lee, and T.-S.Choi, Generalized Millman's formula and its applications for estimation problems, Signal Processing, vol.86, No.2, pp. 257-266, 2006.
[9] J. Zhou, Y. Zhu, Z. You and E. Song, An efficient algorithm for optimal linear estimation fusion in distributed multisensor systems, IEEE Trans. Syst., Man, Cybern., vol.36, no.5, pp. 1000-1009, 2006.
[10] V.Shin "Optimal Linear Fusion of Local Estimates", Proc. IEEE Intern. Conf. Control and Applic., Toronto, Canada, pp. 1435-1440, 2005.
[11] V. Shin, G. Shevlyakov, K. Kim, "A New Fusion Formula and Its Application to Continuous-Time Linear Systems with Multisensor Environment", Computational Statistics & Data Analysis, 2007 (in Press).
[12] S.L.Sun, Z.L.Deng, Multi-Sensor Information Fusion Kalman Filter Weighted by Scalars for Systems with Colored Measurement Noises, Journ. Dynam. Syst, Measurem., Contr., vol. 127, no.12, pp.663-667, 2005.
[13] F.L. Lewis, Optimal Estimation with an Introduction to Stochastic Control Theory, John Wiley & Sons, New York.