Abstract: Icing and related dynamics play important roles in aviation safety. The research on online estimation of aerodynamic parameters of icing aircrafts not only helps to reveal the influence on aerodynamic characteristics of icing, but also improves the capability of icing online identification. Lately Kalman filter and H_∞ algorithm are apply applied on online identification of aircraft icing for their high reliability and fast convergence, even though the assessment of their characteristics under noisy environment are not sufficiently understood. This paper discusses the applications of the extended Kalman filter (EKF) and H_∞ algorithm in the aircraft icing online identification. The methods are validated and evaluated based on the NASA Twin Otter icing research airplane. The three stability and control derivatives in pitch direction are quickly estimated by both two methods from simulation data with gust disturbance and measurement noise. Both two methods estimate these parameters with high precision in 2 seconds, comparing with the reference values. And the state variables filtered by the two methods are consistent with the simulated dynamic process. The results validate the constringency and effectivity of the two methods, together with their potential abilities of applying in the aircraft icing online detection. The identification accuracies of these two methods are evaluated with the simulation data under different measurement noises. With increasing standard deviation in the noise applied in the simulation, the accuracy of EKF method may deteriorate considerably while the accuracy of H_∞ algorithm remains the same level. These results lie on the fact that for EKF the precision rely strongly on precise prior information. On the other hand, H_∞ exhibits better performance under the same circumstances. Its precision is not affected sensitively by bigger standard deviation of measurement noises, which shows more potential gains for online application without high quality prior information.