ENGR205 HM - State Estimation

Credit(s): 3

Instructor(s): Clark

Offered: Fall

Description: This course explores the field of state estimation, and does so through applications in autonomous vehicles. Topics include a review of probability, state or belief representations, and an introduction to several popular filters including Bayes Filters, Kalman Filters, Extended Kalman Filters, Unscented Kalman Filters, and Particle Filters. The course will include a series of labs where students apply the different filters to real data. The course will culminate in a self-designed project in which students must find or collect their own data.

Prerequisite(s): ENGR102 HM