2020
DOI: 10.1109/tac.2020.2989275
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Fully Distributed Resilient State Estimation Based on Distributed Median Solver

Abstract: In this paper, we present a scheme of fully distributed resilient state estimation for linear dynamical systems under sensor attacks. The proposed state observer consists of a network of local observers, where each of them utilizes local measurements and information transmitted from the neighbors. As a fully distributed scheme, it does not necessarily collect a majority of sensing data for the sake of attack identification, while the compromised sensors are eventually identified by the distributed network and … Show more

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Cited by 49 publications
(43 citation statements)
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“…Since ϵ ′′ depends explicitly on ρ, α, and γ, let T * ρ (α, γ) := T • (ϵ ′′ ) ≥ 0. Then, (19) holds for all t ≥ T * ρ (α, γ).…”
Section: Distributed Dynamic P -Quantile Solvermentioning
confidence: 99%
See 1 more Smart Citation
“…Since ϵ ′′ depends explicitly on ρ, α, and γ, let T * ρ (α, γ) := T • (ϵ ′′ ) ≥ 0. Then, (19) holds for all t ≥ T * ρ (α, γ).…”
Section: Distributed Dynamic P -Quantile Solvermentioning
confidence: 99%
“…The intuition behind the distributed algorithm (2) comes from the blended dynamics approach introduced in [17]- [19]. In this approach, we consider the heterogeneous multiagent systems whose dynamics are given by…”
Section: Distributed Dynamic P -Quantile Solvermentioning
confidence: 99%
“…Moreover, they only solve a subset of the DSST problem since they make assumptions either on system dynamics, network, or both. For example, both [18] and [4] make assumptions on system dynamics. In particular, they require the existence of a basis such that the unobservable space of all the (or enough) sensors is the span of a subset of this basis.…”
Section: Introductionmentioning
confidence: 99%
“…Intrinsically, SD enables one to decompose a DSST problem into multiple sub-problems each associated with a scalar system. Interestingly, [18] and [4] exploit SD in different ways which leads to different types of solutions of the DSST problem. In [18], with the help of SD, the DSST problem is formulated as a distributed convex optimization problem with time-varying loss function and a high-gain observer is proposed to reconstruct the state with the help of "blended dynamics approach" introduced in [19].…”
Section: Introductionmentioning
confidence: 99%
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