Friday, 18 10 2019

Theory of Detection and Estimation



Theory of Detection and Estimation
Lesson Code:  22ΜΜ020
Level:  Postgraduate
Semester:  10ο



Overview of Probability Theory and random processes. Binary hypothesis testing according to Bayes and Neymann-Pearson, Min-max test, Uniformly most powerful test, Locally optimum test. Testing of multiple hypotheses, Testing of composite hypotheses, Generalized likelihood ratio test, Test with intermediate decisions. Detection of deterministic signals in noise, Detection of parametric signals, Detection of stochastic signals. Estimation of random parameters according to Bayes, Estimation of unknown parameters, Maximum a-posteriori probability estimator, Maximum likelihood estimator, Extension to many parameters. Unbiased estimates, Cramer-Rao bound, Unbiased minimum variance estimates. Suboptimum estimation and detection techniques, Adaptive estimation. Optimum estimation of random processes, Linear estimation, Orthogonality principle, Non-causal Wiener filter, FIR Wiener filter, Kalman filter. Minimum least squares filter, Recursive least squares (RLS) algorithm. Autoregressive models.

Moustakides George