标题： 基于先验测量支持信息的弹性状态恢复 显示英文标题

标题： Resilient State Recovery using Prior Measurement Support Information

摘要： 弹性状态恢复在网络物理系统中引起了广泛的研究关注，这是由于此类系统中通信、计算和底层物理之间的紧密耦合所带来的独特挑战。 通过将攻击建模为对测量值稀疏子集的加性对抗信号，这种弹性恢复问题可以被表述为一个错误校正问题。 为了实现精确的状态恢复，大多数现有结果要求少于$50\%$的测量节点受到破坏，这限制了估计器的弹性。 在本文中，我们表明通过结合数据驱动的先验信息可以进一步提高观测器的弹性。 我们提供了先验信息精度与估计器弹性之间的分析桥梁。 通过量化加权$\ell_1$观测器的估计误差与支持先验精度之间的关系。 这种量化的关系为实现最佳弹性的估计器权重设计提供了指导。 本文展示了几个数值仿真和一个应用案例研究以验证理论结论。 显示英文摘要

摘要： Resilient state recovery of cyber-physical systems has attracted much research attention due to the unique challenges posed by the tight coupling between communication, computation, and the underlying physics of such systems. By modeling attacks as additive adversary signals to a sparse subset of measurements, this resilient recovery problem can be formulated as an error correction problem. To achieve exact state recovery, most existing results require less than $50\%$ of the measurement nodes to be compromised, which limits the resiliency of the estimators. In this paper, we show that observer resiliency can be further improved by incorporating data-driven prior information. We provide an analytical bridge between the precision of prior information and the resiliency of the estimator. By quantifying the relationship between the estimation error of the weighted $\ell_1$ observer and the precision of the support prior. This quantified relationship provides guidance for the estimator's weight design to achieve optimal resiliency. Several numerical simulations and an application case study are presented to validate the theoretical claims.