Robust Kalman Filter for Position Estimation of Automated Guided Vehicles Under Cyberattacks

Automated guided vehicles (AGVs) have become a key part of many industries, where they handle the task of managing material flows. As a result, they are very important targets for cyberattacks to cripple organizations by using methods, such as false data injection (FDI) and denial of service (DoS) on sensors and measurement data. This article introduces a new robust Kalman filter (KF) for the AGV state estimation using Huber loss function. A statistical method known as M-estimation is used to solve the regression issue robustly and establish the equivalence between the KF and a specific least squares regression problem. For adaptive estimation of the unknown a priori state and observation noise statistics concurrently with the system states, M-robust estimators are developed. The proposed method tackles the state estimation issue against different kinds of cyberattacks, such as pulse, ramp, and DoS cyberattacks. The position estimation using constant velocity tracking based on KF employing with and without the robustification methods is compared. The results confirm the effectiveness and the robustness of the proposed filter approach against the FDI due to the cyberattacks compared with traditional filters. Furthermore, the proposed method is investigated practically with Adlink-ROS AGV.