ECE 64500 - Estimation Theory
Areas of Specialization:
- Communications, Networking, Signal & Image Processing
This course presents the basics of estimation and detection theory that are commonly applied in communications and signal processing systems. Applications in communications and signal processing will be considered throughout.
- Introduction to Statistical Signal Processing With Applications , M.D. Srinath, P.K. Rajasekarau, R. Viswanatha , Prentice-Hall , 1995 , ISBN No. 0-13-125295X
|1||1. Concepts of Estimation and Detection (continued) A. Maximum Likelihood (ML) Estimation 1. The Maximum Likelihood Principle and Maximum Likelihoood Estimation 2. Invariance Principle 3. The Fisher Information Matrix and the Cramer-Rao Lower Bound 4. Efficiency B. Bayesian Estimation 1. Priors, likelihood functions and posterior distributions 2. Bayes Risk and Bayesian estimators 3. Admissibility and risk analysis 4. Noninformative priors, maximum entropy priors and exponential families|
|5||2. Linear Estimation A. Least Squares Estimation 1. Ordinary least squares 2. Covariance factorization and generalized least squares B. Discrete-Time Kalman Filtering 1. System and measurements models 2. Derivations 3. Numerical considerations and square root algorithms C. Continuous-Time MMSE Filtering 1. Calculus of Variation approach 2. Applications in Communications: pretransmission equalizers, random channels, matched filters, multiplicative noise, etc. 3. Cause Filter|
|2||3. Special Topics|