ELL719
| ELL719 | |
|---|---|
| Detection and Estimation Theory | |
| Credits | 3 |
| Structure | 3-0-0 |
| Pre-requisites | ELL711 |
| Overlaps | |
ELL719 : Detection and Estimation Theory
[edit]Overview of the course, Classical Decision Theory: Binary hypothesis testing: Bayes criterion, Neyman-Pearson criterion, min-max test, M-ary hypothesis testing: General rule, minimum probability of error decision rule, Gaussian case and associated geometric concepts, Erasure decision problem, Random parameter estimation. Non – random parameter estimation: CRLB for nonrandom parameters, ML estimation rule, asymptotic properties of ML estimates. Linear minimum variance estimation, Least squares methods CRLB for random parameter estimation, condition for statistical efficiency, Multiple parameter estimation, Composite and non-parametric hypothesis testing, Applications, Detection of signals. Mathematical preliminaries: K-L expansion and its application to Detection of known and un-known (i.e. with unknown, parameters) signals in AWGN., Detection of signals in colored noise. Linear estimation, Wiener filters and solution of Wiener HopfEquations,Kalman-Bucyfilters, Miscellaneous estimation techniques.