Lecture
This lecture covers the application of Kalman Filtering in control and communication systems. It starts with the estimation of position and velocity of a ground vehicle using GPS measurements and then delves into the identification of time-varying wireless channels. The slides illustrate the challenges faced in state estimation, the assumptions made in the linear Gaussian setting, and the recursive computation of filtered states and state predictions. The lecture also discusses the use of Kalman Filtering for channel estimation in wireless communication systems, highlighting the modeling of multipath channels and the estimation of FIR coefficients. Various simulations and examples demonstrate the effectiveness of Kalman Filtering in providing accurate estimations in dynamic systems.