State Estimation and Filtering for Robotic Perception
Lecture
Main Campus
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.
AA 273
Units: 3
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Typically Offered
Not Available
Grading Basis
Letter (ABCD/NP)
Class Level
Graduate
Instructional Mode
In Person
Gen Ed Requirement(s)
None
