Trust-Based Distributed Entropy Filtering for State-Saturated Nonlinear Systems with Hybrid Cyber-Attacks and Non-Gaussian Noises

This article proposes a novel trust-based entropy filter in distributed form for state-saturated nonlinear systems with hybrid cyber-attacks, including denial-of-service and deception attacks. A simple clustering method is employed to categorize the data received from neighboring nodes into two clusters: the trusted cluster and the untrusted cluster. By optimizing a joint cost function involving weighted least squares and a generalized maximum correntropy criterion, a two-step filter is designed in a distributed form, wherein the untrusted data from the neighbors is compensated to relieve the impact from malicious attacks. The significance of the designed algorithm is verified by conducting a target-tracking experiment at the end.