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