Protection-Strategy-Based Distributed State Estimation for Nonlinear Complex Networks Against Random False Data Injection Attacks

Jun Hu

doi:10.1109/jas.2025.125951

Protection-Strategy-Based Distributed State Estimation for Nonlinear Complex Networks Against Random False Data Injection Attacks

2026-08-03

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DOI：10.1109/jas.2025.125951

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This paper addresses the protection-strategy-based distributed state estimation (SE) problem for time-varying non-linear complex networks, where uncertain inner coupling and random false data injection attacks are considered. Owing to the fact that the measurement signals can be easily injected with false data by potential attackers before being transmitted to the estimator, a novel protection strategy is firstly proposed from the perspective of the defender to mitigate the effects of malicious attacks on the estimation performance. Based on the proposed protection strategy, more suspicious and unreliable measurements received are identified and discarded respectively. Accordingly, the zero-order holder strategy is employed to compensate for the discarded measurements. Subsequently, the purpose of this paper is to design a protection-strategy-based distributed SE scheme such that an optimized upper bound (UB) on the estimation error covariance (EEC) is obtained. Furthermore, a sufficient criterion is provided to guarantee the uniform boundedness of UB on the EEC. Finally, a localization problem involving multiple mobile robots is used to demonstrate the effectiveness and practicality of proposed variance-constrained optimized SE method.

Keywords:

Boundedness analysis

protection strategy

random

false data injection attacks

time-varying nonlinear complex networks

uncertain inner coupling (UIC)

Protection-Strategy-Based Distributed State Estimation for Nonlinear Complex Networks Against Random False Data Injection Attacks

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