Alternative to M-Estimates in Multisensor Data Fusion
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:56919", author = "Nga-Viet Nguyen and Georgy Shevlyakov and Vladimir Shin", title = "Alternative to M-Estimates in Multisensor Data Fusion", abstract = "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.", volume = "4", number = "10", pages = "1535-5", }
