Distributed Secure State Estimation for CPSs Based on Side Information

The distributed nature of cyber-physical systems exposes them to severe security threats, where attackers can compromise measurement data through sensor networks, leading to significant physical consequences. Existing research on secure state estimation primarily focuses on optimizing efficiency and improving estimation accuracy, often relying on the strong assumption that the number of compromised sensors does not exceed half of the total, which limits system resilience. To overcome this limitation, this paper proposes a distributed secure state estimation algorithm incorporating side prior information. By integrating side information with a distributed optimization framework, a more resilient secure estimation model is constructed. Theoretical analysis proves the convergence of the algorithm under the $(\Psi,2l)$-observability condition. Numerical simulations demonstrate that the proposed algorithm effectively enhances the system's tolerance to sparse sensor attacks, enabling accurate state estimation even when more than half of the sensor channels are compromised.