[HTML][HTML] Resilient set-based state estimation for linear time-invariant systems using zonotopes
This paper considers the problem of set-based state estimation for linear time-invariant (LTI)
systems under time-varying sensor attacks. Provided that the LTI system is stable and
observable via every single sensor and that at least one sensor is uncompromised, we
guarantee that the true state is always contained in the estimated set. We use zonotopes to
represent these sets for computational efficiency. However, we show that intelligently
designed stealthy attacks may cause exponential growth in the algorithm's worst-case …
systems under time-varying sensor attacks. Provided that the LTI system is stable and
observable via every single sensor and that at least one sensor is uncompromised, we
guarantee that the true state is always contained in the estimated set. We use zonotopes to
represent these sets for computational efficiency. However, we show that intelligently
designed stealthy attacks may cause exponential growth in the algorithm's worst-case …
Resilient set-based state estimation for linear time-invariant systems using zonotopes
This paper considers the problem of set-based state estimation for linear time-invariant (LTI)
systems under time-varying sensor attacks. Provided that the LTI system is stable and
observable via every single sensor and that at least one sensor is uncompromised, we
guarantee that the true state is always contained in the estimated set. We use zonotopes to
represent these sets for computational efficiency. However, we show that intelligently
designed stealthy attacks may cause exponential growth in the algorithm's worst-case …
systems under time-varying sensor attacks. Provided that the LTI system is stable and
observable via every single sensor and that at least one sensor is uncompromised, we
guarantee that the true state is always contained in the estimated set. We use zonotopes to
represent these sets for computational efficiency. However, we show that intelligently
designed stealthy attacks may cause exponential growth in the algorithm's worst-case …
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