Credit points: 3.5

Prerequisites: Random Signal and Noise; Introduction to Digital Signal Processing 

 

Fundamental concepts in estimation theory: Bayesian vs. non-Bayesian estimation, optimality and consistency in estimation; Discrete-time Random Processes; Wide-sense stationary (WSS) processes and their properties; The Spectrum and its statistical meaning and interpretations; Linear parametric processes: Auto-Regressive (AR), Moving Average (MA), ARMA; Spectral estimation: nonparametric methods (periodogram, correlogram, Blackman-Tukey, Welch) and parametric methods, Yule-Walker (YW) and Modified YW equations; Detection of deterministic signals in noise: matched filter; Optimal linear filtering: causal and non-causal Wiener filters, Kalman filter; Introduction to adaptive filtering.