Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32128
Alternative to M-Estimates in Multisensor Data Fusion

Authors: Nga-Viet Nguyen, Georgy Shevlyakov, Vladimir Shin


To solve the problem of multisensor data fusion under non-Gaussian channel noise. The advanced M-estimates are known to be robust solution while trading off some accuracy. In order to improve the estimation accuracy while still maintaining the equivalent robustness, a two-stage robust fusion algorithm is proposed using preliminary rejection of outliers then an optimal linear fusion. The numerical experiments show that the proposed algorithm is equivalent to the M-estimates in the case of uncorrelated local estimates and significantly outperforms the M-estimates when local estimates are correlated.

Keywords: Data fusion, estimation, robustness, M-estimates.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1605


[1] Yunmin Zhu, Multisensor Decision and Estimation Fusion, Kluwer, Boston, MA, 2002.
[2] Yunmin Zhu, Enbin Song, Jie Zhou, and Zhisheng You, "Optimal dimensionality reduction of sensor data in multisensor estimation fusion," Signal Processing, IEEE Transactions on, vol. 53, no. 5, pp. 1631-1639, May 2005.
[3] Yaakov Bar-Shalom and Xiao-Rong Li, Multitarget-multisensor Tracking: Principles and Techniques, YBS Publishing, Storrs, CT, 1995.
[4] Vladimir Shin, Younghee Lee, and Tae S. Choi, "Generalized Millman-s formula and its application for estimation problems," Signal Process., vol. 86, no. 2, pp. 257-266, 2006.
[5] Peter J. Huber, Robust Statistics, Wiley, New York, 1981.
[6] F. R. Hampel, E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel, Robust Statistics: The Approach Based on Influence Functions, Wiley, New York, 1986.
[7] S.A. Kassam and H.V. Poor, "Robust techniques for signal processing: A survey," Proceedings of the IEEE, vol. 73, no. 3, pp. 433-481, March 1985.
[8] Georgy L. Shevlyakov and Nikita O. Vilchevski, Robustness in Data Analysis: Criteria and Methods, VSP, Utrecht, 2002.
[9] U. K. Bhargava and R. L. Kashyap, "Robust parametric approach for impulse response estimation," Acoustics, Speech and Signal Processing, IEEE Transactions on, vol. 36, no. 10, pp. 1592-1601, 1988.
[10] D. D. Lee, R. L. Kashyap, and R. N. Madan, "Robust decentralized direction-of-arrival estimation in contaminated noise," Acoustics, Speech and Signal Processing, IEEE Transactions on, vol. 38, no. 3, pp. 496- 505, 1990.
[11] George A. F. Seber and Alan J. Lee, Linear Regression Analysis, Wiley, New York, 2003.
[12] Vladimir Shin, Georgy Shevlyakov, and Kiseon Kim, "A new fusion formula and its application to continuous-time linear systems with multisensor environment," Comput. Stat. Data Anal., vol. 52, no. 2, pp. 840-854, 2007.
[13] D. F. Andrews, P. J. Bickel, F. R. Hampel, P. J. Huber, W. H. Rogers, and J. W. Tukey, Robust Estimates of Location, Princeton University Press, Princeton, 1972.