ECE: Electrical & Computer Engineering

ECE 5714 Robust Estimation and Filtering


Spring 2014 textbook list

The Spring 2014 ECE textbook list is available online for students.

Current Prerequisites & Course Offering

For current prerequisites for a particular course, and to view course offerings for a particular semester, see the Virginia Tech Course Timetables.

Return to course list

ECE 5714 Robust Estimation and Filtering (3C)

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-breaddown estimators.

What is the reason for this course?

Robust estimation theories have undergone important developments that need to be introduced in various engineering fields such as signal processing, communications, radar systems and electric power systems, to cite a few. The cornerstones of these developments are the robustness concepts of breakdown point and influence function that enable the student to perform the analysis and design of estimators with desired requirements in view of their applications to engineering problems.

Typically offered: Spring. Program Area: Systems/Controls.

Prerequisites: Prerequisites: ECE 5605.

Why are these prerequisites or corequisites required?

This course requires a working knowledge of probability and stochastic processes as taught in 5605.

Department Syllabus Information:

Major Measurable Learning Objectives:
  • Explain maximum likelihood and robust estimation theories and methods;
  • Evaluate the asymptotic efficiency, the breakdown point and the influence function of an M-estimator;
  • Develop maximum likelihood and robust parameter estimation methods in regression and for ARMA models;
  • Develop robust Kalman filters and evaluate their statistical properties;
  • Estimate the parameters of Fractional ARIMA models for long memory processes;
  • Apply maximum likelihood and robust estimation methods to engineering systems.

Course Topics
Topic Percentage
Probability distribution theory 10%
Robust estimators of location and scale 10%
Maximum Likelihood estimation theory and methods 15%
Robust estimation theories and methods 20%
Robust estimators in regression and applications 15%
Robust estimation of ARMA models and applications 10%
Robust Kalman filter and applications 10%
Estimation of Fractional ARIMA models and applications 10%

Return to course list