State Estimation and Filtering for Robotic Perception

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Course Description

Kalman filtering, recursive Bayesian filtering, and nonlinear filter architectures including the extended Kalman filter, particle filter, and unscented Kalman filter. Observer-based state estimation for linear and non-linear systems. Examples from aerospace, including state estimation for fixed-wing aircraft, rotorcraft, spacecraft, and planetary rovers, with applications to control, navigation, and autonomy.

Grading Basis

RLT - Letter (ABCD/NP)

Min

3

Max

3

Course Repeatable for Degree Credit?

No

Course Component

Lecture

Enrollment Optional?

No

Does this course satisfy the University Language Requirement?

No

Programs

AA273 is a completion requirement for: