Statistical Signal Processing AA 2005/06
Introduction: examples of statistical reasoning (2h) [1].
Rudiments of Multivariate Normal Theory (10h) [1].
Estimation Theory: Sufficiency and MVUB Estimators. Maximum
Likelihood Estimators and Cramer-Rao bound; Bayes estimators:
minimum mean-squared error (MMSE); linear MMSE estimators;
Wiener filtering (20 h) [1, 2, 3, 4].
Detection Theory: Neyman-Pearson Lemma, Testing of composite binary
hypotheses, UMP tests, Constant False Alarm Rate property; Bayes
detectors (15h) [1, 2, 3, 4].
Applications to radar signal processing (10h) [2].
Applications to communication theory (15h) [5].
References
[1] L. L. Scharf, ``Statistical Signal Processing: Detection, Estimation,
and Time Series Analysis,’’ Addison-Wesley, 1991.
[2] H. L. Van trees, ``Detection, Estimation and Modulation Theory,’’
Part. 1 e 4, John Wiley & Sons.
[3] S. M. Kay: ``Fundamentals of Statistical Signal Processing, Volume I:
Estimation Theory,’’ Prentice-Hall, 1993.
[4] S. M. Kay: ``Fundamentals of Statistical Signal Processing, Volume I:
Detection Theory,’’ Prentice-Hall, 1998.
[5] U. Mengali, A. N. D’Andrea: ``Synchronization Techniques for
Digital Receivers,’’ Plenum Press, 1997.