Resilient Distributed Static State Estimation for Sensor Networks Against Location-Varying Cyberattacks

This paper studies the problem of resilient distributed state estimation for sensor networks subject to location-varying Byzantine cyber-attacks, focusing on the cooperative estimation of a static vector. The existing mean-subsequence-reduced-based algorithms rely on dimension-wise ordering to resist Byzantine attacks, leading to poor scalability in high-dimensional systems. To overcome this issue, the signum operator and absolute value function are employed to decouple the high-dimensional attack characteristics, based on which a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">norm-based max-discard mechanism</i> (NMDM) is proposed to isolate location-varying Byzantine attacks. Building upon this foundation, a resilient distributed state estimation algorithm integrated with NMDM is developed to achieve consensus estimation of high-dimensional vectors, irrelevant to the switching frequency of attack locations. Finally, two numerical simulation examples are presented to demonstrate the effectiveness and advantages of the proposed algorithm.