Fundamentals of estimation theory as applied to general statistical signal processing applications such as communication systems, image processing, target and position tracking, and machine learning. Estimator fundamentals including probability density functions, Cramer Rao bounds, Fisher information, linear and nonlinear regression, sufficient statistics, maximum likelihood estimation, minimum mean square error, least squares, Bayesian estimators and concepts. Statistical tracking filters such as Kalman filter and particle filter.
This course may not be repeated for credit.
Sections
This course will be offered next in
Winter 2024.