l Optimal Filtering}, Information and System Sciences Series, Prentice--Hall, Englewood Cliffs (NJ) (1979). \smallskip \item{[2]} A.V. Balakrishnan, {\sl Kalman Filtering Theory}, Optimization Software, Inc., New York (NY) (1984). \smallskip \item{[3]} M. Barkat, {\sl Signal Detection and Estimation}, Artech House, Inc., Norwood (MA) (1991). \smallskip \item{[4]} J.O. Berger, {\sl Statistical Decision Theory: Foundations, Concepts and Methods}, Springer Series in Statistics, Springer--Verlag, New York (NY) (1980). \smallskip \item{[5]} R.G. Brown, {\sl Introduction to Random Signal Analysis and Kalman Filtering}, John Wiley \& Sons, New York (NY) (1983). \smallskip \item{[6]} H. Cram\' er, {\sl Mathematical Methods of Statistics} (English Translation), Princeton University Press, Princeton (NJ) (1946). \smallskip \item{[7]} M.H.A. Davis, {\sl Linear Estimation and Stochastic Control}, Chapman and Hall, London (U.K.) (1979). \smallskip \item{[8]} T.S. Ferguson, {\sl Mathematical Statistics: A Decision--Theoretic Approach}, Academic Press, New York (NY) (1967). \smallskip \item{[9]} M.S. Grewal and A.P. Andrews, {\sl Kalman Filtering: Theory and Practice}, Information and System Sciences Series, Prentice--Hall, Englewood Cliffs (NJ) (1993). \smallskip \item{[10]} C.W. Helstrom, {\sl Statistical Theory of Signal Detection}, Pergamon Press, Oxford (U.K.) (1968). \smallskip \item{[11]} D. Kazakos and P. Papantoni--Kazakos, {\sl Detection and Estimation}, Computer Science Press, New York (NY) (1990). E.L. Lehmann, {\sl Testing Statistical Hypotheses}, John Wiley \& Sons, New York (NY) (1950). \smallskip \item{[13]} H.V. Poor, {\sl An introduction to Signal Detection and Estimation}, Graduate Texts in Mathematics {\bf 134}, Springer--Verlag, New York (NY) (1988). \smallskip \item{[14]} A.P. Sage and J.L. Melsa, {\sl Estimation Theory, with Applications to Communications and Control}, McGraw--Hill, New York (NY) (1971). \smallskip \item{[15]} I. Selin, {\sl Detection Theory}, Princeton University Press, Princeton (NJ) (1965). \smallskip \item{[16]} M.D. Srinath and P.K. Rajasekaran, {\sl An Introduction to Statistical Signal Processing with Applications}, John Wiley \& Sons, New York (NY) (1979). \smallskip \item{[17]} H.L. Van Trees, {\sl Detection, Estimation and Modulation Theory, Parts II and III}, John Wiley \& Sons, New York (NY) (1968). \smallskip \item{[18]} A.J. Viterbi and J.K. Omura, {\sl Principles of Digital Communication and Coding}, McGraw--Hill, New York (NY) (1979). \smallskip \item{[19]} A. Wald, {\sl Sequential Analysis}, Dover, New York (NY) (1973). \smallskip \item{[20]} A.D. Whalen, {\sl Detection of Signals in Noise}, Academic Press, New York (NY) (1971). \smallskip \item{[21]} C.L. Weber, {\sl Elements of Detection and Signal Design}, Springer--Verlag, New York (NY) (1987). \smallskip \item{[22]} J.M. Wozencraft and I.M. Jacobs, {\sl Principles of Communication Engineering}, John Wiley \& Sons, New York (NY) (1965) Additional information can be found in various issues of the following journals: \smallskip \settabs 8 \columns \+&& {\sl IEEE Transactions on Communications} \cr %\+&& {\sl IEEE Journal on Selected Areas in Communications} \cr \+&& {\sl IEEE Transactions on Information Theory} \cr \+&& {\sl Problems of Information Transmission} \cr \+&& (English translation of {\sl Problemy Pereda\v{c}i Informacii)} \cr nterline{\bf SELECTION OF TOPICS} \bigskip \centerline{\bf Estimation Theory} \medskip {\bf 1.} Non--Bayesian parameter estimation: Maximum likelihood estimation. \medskip {\bf 2.} Properties of estimators: Sufficient statistics, bias, consistency, efficiency; Cram\'er--Rao bounds, asymptotic efficiency and normality, minimum variance unbiased estimators. \medskip {\bf 3.} Bayesian parameter estimation: Mean--squared error and maximum a posteriori probability criteria. \medskip {\bf 4.} Linear least--squares estimation: Projection theorem, properties of linear estimators; Kalman filtering. \medskip \centerline{\bf Detection Theory} \medskip {\bf 1.} Hypothesis testing: Likelihood ratio, Bayes' criterion, minimax criterion, Neyman--Pearson criterion, sufficient statistics, performance evaluation -- receiver operating characteristics (ROC). \medskip {\bf 2.} Multiple hypothesis testing. \medskip {\bf 3.} Composite hypothesis testing: Generalized likelihood ratio, uniformly most powerful tests. \medskip {\bf 4.} Sequential detection: Wald's test. \medskip {\bf 5.} Detection of signals in noise: Discrete--time and continuous--time. \medskip {\bf 6.} Detection of known signals in white noise: Discrete--time approximations -- Brownian motion approach and bandlimited approach; correlation receiver, matched filter receiver. \medskip {\bf 7.} Detection of known signals in colored noise: Karh\" unen--Lo\'eve expansion, whiltening filter approach, singular detection. \medskip {\bf 8.} Detection of known signals in noise: Signal--to--noise ratio criterion. \medskip {\bf 9.} Detection of signals with unknown parameters: Deterministic and random parameters. A.P. Sage and J.L. Melsa, {\sl Estimation Theory, with Applications to Communications and Control}, McGraw--Hill, New York (NY) (1971).