Student’s t ‐Kernel‐Based Graph Signals Maximum Correntropy Unscented Kalman Filter Under Hybrid Cyberattacks

The implementation of Kalman filter (KF) in tracking high‐dimensional, strongly correlated graph structured data is often complex and unstable. Meanwhile, in practical applications, the system may be subject to interference from non‐Gaussian noise and various cyberattacks. First, the Student’s t ‐kernel‐based graph signals maximum correntropy unscented KF (ST‐GMCUKF) is proposed for hybrid attacks and non‐Gaussian noise. The considered hybrid cyberattacks include denial of service (DoS) attacks and deception attacks. Then, the method integrates graph Fourier transform (GFT) to diagonalize Kalman gains for vertex‐wise independent updates, reducing cumulative errors. Furthermore, by replacing Gaussian kernel with Student’s t ‐kernel in the maximum correntropy criterion (MCC), it enhances robustness against heavy‐tailed noise and outliers. Finally, the estimation performance of the ST‐GMCUKF algorithm is evaluated using a univariate nonstationary growth model. Simulation results demonstrate its superior capability in tracking high‐dimensional graph signals systems under non‐Gaussian noise and hybrid cyberattacks.