| Topics will cover discrete time stochastic processes; stochastic state space models; Yule-Walker equations; stationary discrete time stochastic processes; characterization of stochastic processes; correlation matrix; power spectral density; least square estimation; minimum variance and linear minimum variance estimation, orthogonality and projection; the normal equation; minimum mean-squared error; optimum non recursive filter; optimum recursive filter; Kalman filter; innovation sequence; adaptive algorithms; finite impulse response filters; recursive least-squares algorithms, least mean squares adaptive algorithm; steepest gradient; Newton and Conjugate gradient algorithms; etc.; noise cancellation; inverse modeling; identification |
