Course Description
This course examines the fundamentals of detection and estimation for signal processing, communications, and control. Topics covered include: vector spaces of random variables; Bayesian and Neyman-Pearson hypothesis testing; Bayesian and nonrandom parameter estimation; minimum-variance unbiased estimators and the …
This course examines the fundamentals of detection and estimation for signal processing, communications, and control. Topics covered include: vector spaces of random variables; Bayesian and Neyman-Pearson hypothesis testing; Bayesian and nonrandom parameter estimation; minimum-variance unbiased estimators and the Cramer-Rao bounds; representations for stochastic processes, shaping and whitening filters, and Karhunen-Loeve expansions; and detection and estimation from waveform observations. Advanced topics include: linear prediction and spectral estimation, and Wiener and Kalman filters.
Course Info
![Graph showing threshold phenomenon in nonlinear estimation.](/courses/6-432-stochastic-processes-detection-and-estimation-spring-2004/47903e1c28a3865162b3c477757c56fb_6-432s04.jpg)
Example of threshold phenomenon in nonlinear estimation. (Image courtesy of Alan Willsky and Gregory Wornell.)