Robust Lattice Kalman Filter Under False Data Injection Attacks and Measurement Data Loss

In this paper, a novel model under false data injection (FDI) attack and measurement loss is established, and it is combined with lattice Kalman filter (LKF). LKF based on loss and generalized measurement model (LGMM) is proposed as an effective method for dealing with simultaneous measurement loss and FDI attacks. LGMM can be extended to other nonlinear Kalman filters, such as extended Kalman filter (EKF), unscented Kalman filter (UKF), and cubature Kalman filter (CKF). Furthermore, in order to make the estimation more accurate, a back out step is added to the framework. The robustness and effectiveness of the proposed method is demonstrated through numerical simulations.