Robust State Estimation in the Presence of Stealthy Cyberattacks

Khan, Shiraz; Hwang, Inseok; Goppert, James

doi:10.23919/acc53348.2022.9867774

Cited by 2 publications

(3 citation statements)

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“…where the last equality follows from the construction of G 0 . Using Equation (20), we arrive at the desired inequality (19). ■ Clearly, φ max ðX ; rÞ bounds the minimal bandwidth φ min ðGÞ of a geometric graph as well.…”

Section: ¼ Maxmentioning

confidence: 99%

“…■ Clearly, φ max ðX ; rÞ bounds the minimal bandwidth φ min ðGÞ of a geometric graph as well. The following corollary shows that this bound can be further improved by minimising (19) over the set of realisations that induce the same edge set E.…”

Section: ¼ Maxmentioning

confidence: 99%

“…A drawback of classical state estimation algorithms is that they only track the state estimate, whereas Bayesian estimators (such as those based on Kalman filtering theory) estimate the covariance of the estimation error as well. The error covariance is valuable for control and decision applications, such as checking for sensor faults using a residual test [19]. Another advantage of Kalman filter-based approaches is that they have provable convergence guarantees, which is not the case for classical estimators that only guarantee the asymptotic consistency of the estimate [32,35].…”

Section: Introductionmentioning

confidence: 99%

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Exploiting sparsity for localisation of large‐scale wireless sensor networks

Khan,

Hwang,

Goppert

2024

IET Wireless Sensor Systems

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Wireless Sensor Network (WSN) localisation refers to the problem of determining the position of each of the agents in a WSN using noisy measurement information. In many cases, such as in distance and bearing‐based localisation, the measurement model is a non‐linear function of the agents' positions, leading to pairwise interconnections between the agents. As the optimal solution for the WSN localisation problem is known to be computationally expensive in these cases, an efficient approximation is desired. The authors show that the inherent sparsity in this problem can be exploited to greatly reduce the computational effort of using an Extended Kalman Filter (EKF) for large‐scale WSN localisation. In the proposed method, which the authors call the L‐Banded Extended Kalman Filter (LB‐EKF), the measurement information matrix is converted into a banded matrix by relabelling (permuting the order of) the vertices of the graph. Using a combination of theoretical analysis and numerical simulations, it is shown that typical WSN configurations (which can be modelled as random geometric graphs) can be localised in a scalable manner using the proposed LB‐EKF approach.

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Section: ¼ Maxmentioning

confidence: 99%

Section: ¼ Maxmentioning

confidence: 99%