网络物理系统在传感器偏差下的在线稀疏观测器
Online sparse observers for cyber-physical systems under sensor bias
AI总结：
研究网络物理系统在传感器偏差下的在线稀疏观测器问题，通过分析恒定攻击的可观测性，从不同方法框架研究多种在线稀疏观测器，统一概述可实际应用的观测器，讨论收敛特性并比较性能。
AI中文摘要：
近年来，针对存在稀疏传感器偏差、故障或攻击的网络物理系统的状态观测器设计备受关注。基于稀疏性的批处理方法相对成熟，但在线安全状态估计仍是一个开放问题，虽有算法提出，但收敛保证有限。本文首先关注恒定攻击，分析其可观测性并研究多种在线稀疏观测器，包括源于不同方法框架的观测器。部分观测器是对现有算法的改进，部分是新贡献。本文旨在统一概述可实际应用的在线稀疏观测器，讨论其收敛特性并通过数值实验比较性能。
英文摘要：
The design of state observers for cyber-physical systems under sparse sensor biases, faults, or attacks has drawn substantial attention in recent years. While sparsity-based batch approaches, which collect multiple measurements and run offline, are relatively mature, online secure state estimation, for real-time state/attack recovery, is still an open problem. Although some algorithms have been proposed, convergence guarantees remain limited, even for the case of constant attacks. As a first step toward addressing this gap, we focus on constant attacks. We analyze the observability of this setting and we study several online sparse observers, derived from different methodological frameworks such as online sparse optimization and block Bregman methods. Some of the proposed observers are adaptations of existing algorithms to the secure state-estimation setting, while others constitute novel algorithmic contributions. The goal of this paper is to provide a unified overview of practically implementable, online sparse observers, discuss their convergence properties, and compare their performance through numerical experiments.
