Title: Statistical Linear Regression Approach to Kalman Filtering and Smoothing under Cyber-Attacks Show Chinese title

Title: 网络攻击下的卡尔曼滤波和平滑统计线性回归方法

Abstract: Remote state estimation in cyber-physical systems is often vulnerable to cyber-attacks due to wireless connections between sensors and computing units. In such scenarios, adversaries compromise the system by injecting false data or blocking measurement transmissions via denial-of-service attacks, distorting sensor readings. This paper develops a Kalman filter and Rauch--Tung--Striebel (RTS) smoother for linear stochastic state-space models subject to cyber-attacked measurements. We approximate the faulty measurement model via generalized statistical linear regression (GSLR). The GSLR-based approximated measurement model is then used to develop a Kalman filter and RTS smoother for the problem. The effectiveness of the proposed algorithms under cyber-attacks is demonstrated through a simulated aircraft tracking experiment. Show Chinese abstract

Abstract: 在信息物理系统中，由于传感器与计算单元之间的无线连接，远程状态估计通常容易受到网络攻击的影响。在这种情况下，攻击者通过注入虚假数据或通过拒绝服务攻击阻塞测量传输，从而篡改传感器读数，破坏系统。本文针对受网络攻击测量值影响的线性随机状态空间模型，开发了一种卡尔曼滤波器和Rauch--Tung--Striebel（RTS）平滑器。我们通过广义统计线性回归（GSLR）近似故障测量模型。然后利用基于GSLR的近似测量模型来构建卡尔曼滤波器和RTS平滑器以解决该问题。通过模拟的飞机跟踪实验验证了所提出算法在遭受网络攻击时的有效性。