Detection and Estimation Theory

EEE 539 is the graduate-level framework for making optimal decisions and extracting unknown quantities from noisy observations — the mathematical backbone behind radar, communications receivers, and modern statistical signal processing. You work through Poor's textbook deriving decision rules and estimators under different criteria (Bayesian, minimax, Neyman-Pearson, ML), with four problem sets, a midterm and final, and a project that culminates in Kalman-Bucy filtering. Expect a probability-heavy course that formalizes intuitions from undergraduate signals and statistics, and serves as the prerequisite mindset for research in detection, estimation, and inference-driven EE topics.