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