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).