Detection and Estimation
Introduction to detection and estimation theory with applications. Topics include: maximum likelihood and Bayesian estimates, Kalman filtering, simple and composite hypothesis testing, and detection of signals in noise.
Introduction to detection and estimation theory with applications. Topics include: maximum likelihood and Bayesian estimates, Kalman filtering, simple and composite hypothesis testing, and detection of signals in noise.