Linear Estimation and Prediction

This lecture covers the principles of linear estimation and prediction in signal processing, focusing on finding the signal in noisy signals, constructing linear filters, minimizing mean square error, and obtaining optimal filtering using Wiener filters. It also discusses the Wiener-Hopf equations, orthogonality, and the geometric interpretation of linear estimates. The lecture explores the Wiener filtering and prediction, the use of single pole filters, and the Wiener-Hopf equations for white noise. Additionally, it delves into the minimization of quadratic error, the Kalman filters for dynamical systems, and the importance of auto/inter-correlation functions in optimal linear prediction.