ﻻ يوجد ملخص باللغة العربية
We consider the distributed $H_infty$ estimation problem with an additional requirement of resilience to biasing attacks. An attack scenario is considered where an adversary misappropriates some of the observer nodes and injects biasing signals into observer dynamics. The paper proposes a procedure for the derivation of a distributed observer which endows each node with an attack detector which also functions as an attack compensating feedback controller for the main observer. Connecting these controlled observers into a network results in a distributed observer whose nodes produce unbiased robust estimates of the plant. We show that the gains for each controlled observer in the network can be computed in a decentralized fashion, thus reducing vulnerability of the network.
We consider the distributed $H_infty$ estimation problem with additional requirement of resilience to biasing attacks. An attack scenario is considered where an adversary misappropriates some of the observer nodes and injects biasing signals into obs
We develop a decentralized $H_infty$ synthesis approach to detection of biasing misappropriation attacks on distributed observers. Its starting point is to equip the observer with an attack model which is then used in the design of attack detectors.
We study how to secure distributed filters for linear time-invariant systems with bounded noise under false-data injection attacks. A malicious attacker is able to arbitrarily manipulate the observations for a time-varying and unknown subset of the s
The paper addresses the problem of detecting attacks on distributed estimator networks that aim to intentionally bias process estimates produced by the network. It provides a sufficient condition, in terms of the feasibility of certain linear matrix
In this paper, we study the resilient vector consensus problem in networks with adversarial agents and improve resilience guarantees of existing algorithms. A common approach to achieving resilient vector consensus is that every non-adversarial (or n