Performance analysis of state estimators for a multivariable process

State estimation techniques play a great role in providing insight into plant dynamics by providing information about the quality variables in the system. For multivariable systems, typically few variables are directly measured, while others can be estimated. The knowledge of state variables is extremely important for implementing model based control or state feedback control. In this paper we present comparison of performances of Kalman Filter (KF) and Extended Kalman Filter (EKF) on a benchmark four tank process. Simulation results show the various performance parameters for both KF and EKF.