LPC Approaches to Compensate Missing Measurements in Kalman Filtering - KHAN, NAEEM
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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.…mehr

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Produktbeschreibung
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.
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Autorenporträt
Dr. Naeem Khan born in Bannu city, Province KPK, Pakistan on March 20, 1980. He recieved BSc and MSc from UET Peshawar (Pak) and PhD in Control Engineering from University of Leicester, UK. During his PhD, he has published several conference and journal papers. His interest includes control of linear & nonlinear systems, robust state estimation etc