ECEN 5652 - Detection and Extraction of Signals from Noise
ECEN 5652 (3). Detection and Extraction of Signals from Noise.
estimation and time seriesanalysis. Topics include hypothesis testing,
detection of known form and random signals, least squares parameter
estimation, maximum likelihood theory, minimum mean-squared error
estimation, Kalman-Wiener filtering, prediction in stationary time
series, and modal analysis. Applications include studies
in communications, control, and experimental modeling.
Credits and Design
||3 credit hours. Elective course.
ECEN 5612, Noise and Random Processes
Statistical Signal Processing, L. Scharf, 1991.
- An Introduction to Detection and Estimation,
V.H. Poor, Springer-Verlag, 1989.
- Detection, Estimation and Modulation Theory,
H.L. VanTrees, Wiley, 1968.
- Principles of Communication Engineering,
Wozencraft and Jacobs, Wiley, 1965.
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Understanding of the fundamentals of
hypothesis testing and estimation and their engineering applications in
various signal detection and extraction problems.
- Review of applied probability
and random processes, the Karhunen-Loeve expansion. Statistical modeling
and an introduction to detection and estimation problems.
of linear algebra: vector spaces, linear independence,
QR factorizations, linear subspaces, singular value decompositions,
projections, rotations, psuedoinverses.
- Detection theory: simple
hypothesis testing under the Bayes' criterion and the Neymann-Pearson
criterion; sufficient statistics. Composite hypothesis testing, the
Neymann-Pearson criterion and the notion of invariance. The generalized
likelihood ratio test and its optimality. Minimax detection
and sequential detection. Applications include detection problems
in communications and radar/sonar signal processing including the
linear statistical model and the multivariate Gaussian
- Estimation theory: maximum likelihood estimation and
sufficiency, Cramer-Rao inequality, Bayesian and minimax parameter
estimation including minimum mean-squared error and maximum a posteriori
estimation, linear minimum mean-squared estimation; applications
including the multivariate normal model, linear statistical model, Kalman
and Wiener filtering.
Last revised: 08-02-11, PM, ARP.