EC3310 Optimal Estimation: Sensor and Data Association

The subject of this course is optimal estimation and Kalman filtering with extensions to sensor fusion and data association. Main topics include the theory of optimal and recursive estimation in linear (Kalman filter) and nonlinear (extended Kalman filter) systems, with applications to target tracking. Topics directly related to applications, such as basic properties of sensors, target tracking models, multihypothesis data association algorithms, reduced order probabilistic models and heuristic techniques, will also be discussed. Examples and projects will be drawn from radar, EW, and ASW systems.

Prerequisite

EC2320, EC2010, MA2043 or consent of instructor.

Lecture Hours

3

Lab Hours

2

Security Clearance Required

• Secret

Course Learning Outcomes

·       Theory of Least Squares Estimation in Linear Models

·       Kalman Filter Theory: Optimal Estimation in Linear Dynamic Systems

·       Extended Kalman Filter Theory in Nonlinear Dynamic Systems

·       Applications of the theory in Sensor Fusion and Target Tracking