ECE-5714: Robust Estimation and Filtering
Description: An introduction to the analysis and design of maximum likelihood and robust estimators and filters. Maximum likelihood estimation theory: consistency, asymptotic efficiency, sufficiency. Robust estimation theory: qualitative robustness, breakdown point, influence function, change-of-variance function. Robust estimators: M-estimators, generalized M-estimators, high-breakdown estimators. Robust estimation of ARIMA models; Robust Kalman filter. Long memory processes: Hurst parameter estimation; parameter estimation of fractional ARIMA models. Applications to image and speech processing, communications, radar systems, and electric power systems.
Pathways: N/A
Course Hours: 3 credits
Corequisites: N/A
Crosslist: N/A
Repeatability: N/A
Sections Taught: 21
Average GPA: 3.50 (A-)
Strict A Rate (No A-) : 34.39%
Average Withdrawal Rate: 0.00%
| Lamine M Mili | 2024 | 47.5% | 51.8% | 0.0% | 0.7% | 0.0% | 0.0% | 3.50 | 21 |