DETECTION AND ESTIMATION THEORY
Learning outcomes of the course unit
To describe the theoretical foundation of detection and estimation theory with application to digital communication systems.
Course contents summary
Discrete representation of deterministic and random signals.
Detection theory--Statistic model for detection. MAP criterion. Detection in the presence of additive white Gaussian noise. Sufficient statistics. Matched filter. Detection in the presence of additive Gaussian colored noise: reversibility theorem. Detection in the presence of random parameters.
Estimation theory--Statistic model for estimation. Estimation of deterministic parameters: ML criterion. Estimation of stochastic parameters: Bayes criterion. Cramer-Rao inequality. Minimum mean square linear estimation. Wiener filter. Prediction. Kalman filter.
Recommended readings
G. Colavolpe, R. Raheli, Lezioni di Trasmissione numerica, Monte Università Parma editore, 2004.H. L. Van Trees, Detection, estimation and modulation theory, Part I, John Wiley and Sons, 2001.
