Secure dynamic state estimation by decomposing Kalman filter
We consider the problem of estimating the state of a linear time-invariant Gaussian system in the presence of sparse integrity attacks. The attacker can control p out of m sensors and arbitrarily change the measurements. Under mild assumptions, we can decompose the optimal Kalman estimate as a weigh...
Main Authors: | , , |
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Other Authors: | |
Format: | Journal Article |
Language: | English |
Published: |
2018
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/87830 http://hdl.handle.net/10220/46831 |
Summary: | We consider the problem of estimating the state of a linear time-invariant Gaussian system in the presence of sparse integrity attacks. The attacker can control p out of m sensors and arbitrarily change the measurements. Under mild assumptions, we can decompose the optimal Kalman estimate as a weighted sum of local state estimates, each of which is derived using only the measurements from a single sensor. Furthermore, we propose a convex optimization based approach, instead of the weighted sum approach, to combine the local estimate into a more secure state estimate. It is shown that our proposed estimator coincides with the Kalman estimator with certain probability when all sensors are benign, and we provide a sufficient condition under which the estimator is stable against the (p, m)-sparse attack when p sensors are compromised. A numerical example is provided to illustrate the performance of the proposed state estimation scheme. |
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