Course Information

EL6333 Detection and Estimation Theory

Credits: 3.00

Description:
Detection Theory: Binary Hypothesis Testing; Bayes’ Criteria; Likelihood Ratio Test; min-max test; Neyman-Pearson Tests; Receiver Operating Characteristics. Parameter Estimation Theory: Random parameter Estimation; Bayes’ Procedure; Minimum Mean Square Error (MMSE) Estimator; Maximum A-Posteriori (MAP) Estimator. Nonrandom Parameter Estimation: MAP Estimator; Unbiased Estimators and Cramer-Rao(C-R) Bound; Higher Order Bhattacharya Bounds.Uniformly Minimum Variance Unbiased Estimators (UMVUE); Sufficient Statistic; Factorization Theorem; Rao-Blackwell Theorem. Multi-Parameter Estimation; Fisher Information Matrix. Composite Hypothesis Testing; Series Representation of Stochastic Processes with Rational spectra; Detection of distinct signals in white noise and colored noise; Mary Detection and Estimation of signals in white noise and colored noise. Blind Channel Identification. Elements of signal design for white Gaussian noise. M-ary waveform design for two-dimensional signals.

Prerequisites: Graduate status and EL 6303.