This work aim at improving the prediction of experts aggregation by using the underlying characteristics of the models that provide the experts predictions. We restrict ourselves to experts predictions coming from Kalman recursions on state-space models. By using exponential weights, we construct different Kalman recursions Aggregated Online (KAO) algorithms that compete with the best expert or the best convex combination of experts in a more or less adaptive way. We improve the existing results on experts aggregation literature when the experts are Kalman recursions by taking advantage of the second order characteristics of the Kalman recursions. We apply our approach to Kalman recursions and extend it to the general adversarial experts setting by state-space modeling the errors of the experts.