Stanford Root

Schedule

Stanford Root

Schedule

AA 273

State Estimation and Filtering for Robotic Perception

UNITS:3
GRADING:Letter (ABCD/NP)
LEVEL:Graduate
GER:—

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.

Syllabus for selected term:
View Winter 2027 Syllabus

Sections

1 Term
Lecture 1Open
ID: 6391
0 / 70 enrolled
DAYS:Tuesday, Thursday
TIME:1:30 PM – 2:50 PM
LOCATION:TBD
INSTRUCTOR:
Neamati, Daniel
3units

AA 273: State Estimation and Filtering for Robotic Perception

3 units · Letter (ABCD/NP)

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.

Offered in Winter 2027 at Stanford University.

Winter 2027 sections

  • Lecture — Tuesday Thursday 1:30 PM – 2:50 PM — Neamati, Daniel (Graduate)

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