Learning objectives
<br /><br />To describe the theoretical foundation of detection and estimation theory with application to digital communication systems.
Course unit content
<br /><br />Discrete representation of deterministic and random signals.<br /> <br />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.<br /> <br />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.
Bibliography
<br /><br />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.