μ-stealthy deception attack against distributed state estimation

Dancheng Mao

doi:10.1016/j.nahs.2026.101725

μ-stealthy deception attack against distributed state estimation

2026-04-14

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DOI：10.1016/j.nahs.2026.101725

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This research focuses on the design of a μ-stealthy attack strategy against distributed state estimation, achieving the balance between attack effectiveness and stealthiness. By fusing innovations from local and neighboring sensors, the steady-state filter gain and estimation error covariance (EEC) are derived, and the innovation properties are analyzed for targeted attacks. An attack model based on neighboring innovations is proposed, and the worst-case attack parameters are determined stepwise through equivalent transformation of the optimization problem, addressing the nonlinear optimization challenges posed by distributed estimation coupling. Simulations validate the optimality and stealthiness of our attack strategy, providing valuable insights for secure estimation design.

Keywords:

μ-stealthy attack

distributed state estimation

innovation-based attack

steady-state filter gain

estimation error covariance

μ-stealthy deception attack against distributed state estimation

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