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An attack-resilient distributed extended Kalman consensus filtering with application to multi-UAV tracking problems
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Yuru HU, Wangyan LI, Lifeng WU, Zhensheng YU. An attack-resilient distributed extended Kalman consensus filtering with application to multi-UAV tracking problems[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="An attack-resilient distributed extended Kalman consensus filtering with application to multi-UAV tracking problems",
author="Yuru HU, Wangyan LI, Lifeng WU, Zhensheng YU",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300621"
}
%0 Journal Article
%T An attack-resilient distributed extended Kalman consensus filtering with application to multi-UAV tracking problems
%A Yuru HU
%A Wangyan LI
%A Lifeng WU
%A Zhensheng YU
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
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%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300621
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T1 - An attack-resilient distributed extended Kalman consensus filtering with application to multi-UAV tracking problems
A1 - Yuru HU
A1 - Wangyan LI
A1 - Lifeng WU
A1 - Zhensheng YU
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
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%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2300621
Abstract: This study investigates as to how the events of deception attacks are distributed during the fusion of multi-sensor nonlinear systems. Firstly, a deception attack with limited energy (DALE) is introduced under the framework of distributed extended Kalman consensus filtering (DEKCF). Next, a hypothesis testing-based mechanism to detect the abnormal data generated by DALE, in the presence of the error term caused by the linearization of the nonlinear system, is established. Once the DALE is detected, a new rectification strategy can be triggered to recalibrate the abnormal data, restoring it to its normal state. Then, an attack-resilient distributed extended Kalman consensus filtering (AR-DEKCF) algorithm has been proposed, and its fusion estimation errors have been demonstrated to satisfy the mean square exponential boundedness performance, under appropriate conditions. Finally, the effectiveness of which has been confirmed through simulations involving multi-unmanned aerial vehicle (multi-UAV) tracking problems.
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