Multi-Rate Sampled-Data Secure Fusion Estimation Against Malicious Hybrid Attacks
Haiyu Song, Siqing Ye, Peng Jia Shi, Wen‐An Zhang, Li Juan Yu · IEEE Transactions on Signal and Information Processing over Networks · 2025
This paper investigates the Kalman fusion estimation problem for multi-sensor systems based on multi-rate sampled data within a non-secure network environment. For each sensor, an innovative multi-rate sampling estimation module is proposed, allowing for multiple samplings within a single estimation cycle to gather as much sampled information as possible. The sampled data during transmission is thought to encounter three potential scenarios: being subjected to DoS attack, FDI attack, or undergoing normal transmission. These three potential scenarios are modeled as a random phenomenon described by two sets of Bernoulli variables. A unified information framework is subsequently introduced, adept at encompassing the three attack scenarios along with the multi-rate sampling process. This framework serves as the basis for the design of a local secure Kalman estimator, followed by stability analysis. Finally, a distributed secure fusion estimation algorithm is proposed, and its effectiveness is demonstrated through a simulation example.