Lpc Approaches to Compensate Missing Measurements in Kalman Filtering

State Estimation is a nontrivial case of study in both control and communication. In the last decade it has gained popularity due to enoumrous research in this area. The only technique employed for estimation for the incomplete and missing data is Open loop estimation where the state is predicted during the lossy time period. In this work, a novel approach is employed for stationary and non stationary process through Linear Prediction scheme. The missing data is first reconstructed through Modified External Linear Prediction Coefficient method and then employed in the state estimation process. Case studies have been performed in order to test the superiority of the proposed method.