Course Information

EL6333 Detection and Estimation Theory

Credits: 3.00

Description:
Binary hypothesis testing and Bayes’ criteria; Receiver operating characteristics; Composite hypothesis testing. Parameter estimation theory - Random parameter estimation; Minimum mean square error (MMSE) estimation; Maximum a-posteriori (MAP) estimation; Nonrandom parameter estimation; Minimum variance unbiased estimators; Cramer-Rao bound and Rao-Blackwell theorem; Multiple parameter estimation and Fisher information matrix. Series representation of stochastic processes; Karhunen Loeve (K-L) expansion of a stochastic process over a finite time. Stationary stochastic processes; Autocorrelation function and power spectrum; Spectrum extension problem from finite autocorrelations; Maximum entropy solution and auto regressive processes. Direction of arrival (DoA) estimation using multiple sensors; Detection of distinct signals in white noise and colored noise.

Prerequisites: EL 6303.