Optimal Estimation: Sensor and Data Association

Course #EC3310

Est.imated Completion Time: 3 months

Overview

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.

Security clearance: Secret

Included in degrees & certificates

284

Prerequisites

EC2320
EC2010
MA2043

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

Offerings database access

Asset Publisher

Application Deadlines

08 Jul 2024

Fall Quarter applications due