Resilient Distributed State Estimation for LTI Systems Under Time-Varying Deception Attacks
Bibliographic record
Abstract
This article studies the resilient distributed state estimation over the sensor networks under measurement attacks, which make the measurements of variant subsets of sensors aberrant at different time instants. For this problem, while most of the existing works focus on the static target states that do not change over time, we investigate the estimation for the dynamic ones, which evolve according to general linear time-invariant (LTI) systems. To achieve the resilient distributed state estimation for the general LTI systems under the measurement attacks, we propose a dynamic-target regulative gain estimation (DTRGE) algorithm, in which an attack detector, a regulative gain matrix, and an adaptive gain are designed. The detector helps agents monitor the measurement anomalies, and once the attacks are detected, the adaptive gain can counteract the deviation of the estimates induced by them. The regulative gain matrix restrains the negative effects on the convergence of the estimates caused by the system matrix of the target LTI system, especially the unstable one. We demonstrate that all the sensors can recover the target state by running the DTRGE algorithm, if the topology and the observability of the sensor network satisfy certain conditions. Moreover, we further apply the DTRGE algorithm to the sensor networks with switching topologies, and demonstrate that the estimation task can also be completed by these sensors. Finally, simulation and experiment results are given to illustrate the performance of the DTRGE algorithm.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".