State estimation is the precondition and foundation of a bioprocess monitoring and optimal control. However, there are many difficulties in dealing with a non-linear system, such as the instability of process, un-modeled dynamics, parameter sensitivity, etc. This paper discusses the principles and characteristics of three different approaches, extended Kalman filters, strong tracking filters and unscented transformation based Kalman filters. By introducing the unscented transformation method and a sub-optimal fading factor to correct the prediction error covariance, an improved Kalman filter, unscented transformation based robust Kalman filter, is proposed. The performance of the algorithm is compared with the strong tracking filter and unscented transformation based Kalman filter and illustrated in a typical case study for glutathione fermentation process. The results show that the proposed algorithm presents better accuracy and stability on the state estimation in numerical calculations.