Set-membership multi-sensor secure fusion estimation against two-channel malicious attacks

In this study, the set-membership secure fusion estimation problem against malicious attacks is addressed. In practice, adversaries are able to tamper with the measurement outputs and local estimates from different communication channels. By rewriting the attacked information into two parts that are related and unrelated to the initial value, a novel secure estimation model is developed to describe the multi-sensor fusion systems that are subject to two-channel bounded attacks. An existence condition of the set-membership secure fusion estimator is presented to guarantee that the secure fusion estimation errors are always confined to expected ellipsoids. Subsequently, the secure fusion weight matrices are determined by solving a constructed convex optimization problem constrained by a linear matrix inequality condition. Lastly, the rationality of the designed set-membership secure fusion estimation algorithm is verified using two simulation examples.