Statistical Signal Processing (EEE515Z): Course Details

Introduces detection, estimation and aspects of time-series analysis. The emphasis will be on statistical signal processing in discrete-time, although the parallels with continuous-time will be outlined. There is a strong practical component to the course. Topics that will be approached are

Detection theory (Hypothesis testing):

Simple hypothesis testing (Bayes and Neyman-Pearson criteria), sufficient statistics.
Composite hypothesis testing, the Neyman-Pearson criterion and the notion of invariance.
Applications: detection problems in communications and radar/sonar signal processing.

Estimation theory:

Maximum likelihood estimation and sufficiency.
Bayesian and minimax parameter estimation (including minimum mean-squared error and maximum a posteriori estimation).
Linear minimum mean-squared estimation.
Applications: Kalman and Weiner filtering.

There is no prescribed text, but useful reference books will be:

L.L. Scharf: Statistical Signal Processing: Detection, Estimation, and Time Series Analysis, Addison-Wesley 1990
S.M. Kay: Fundamentals of Statistical Signal Processing: Estimation Theory, Prentice Hall 1993
H.L. van Trees: Detection, Estimation, and Modulation Theory: Part I, Wiley 1968
D. Kazakos, P. Papantoni-Kazakos: Detection and Estimation, Computer Science Press 1990
Y. Bar-Shalom, X. Li: Estimation and Tracking: Principles, Techniques, and Software, Artech House 1993
M. Schwartz and L. Shaw: Signal Processing: Discrete Spectral Analysis, Detection, and Estimation, McGraw-Hill 1981

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