As a follow-on course to "Linear Kalman Filter Deep Dive", this course derives the steps of the extended Kalman filter and the sigma-point Kalman filter for estimating the state of nonlinear dynamic systems. You will learn how to implement these filters in Octave code and compare their results. You will be introduced to adaptive methods to tune Kalman-filter noise-uncertainty covariances online. You will learn how to estimate the parameters of a state-space model using nonlinear Kalman filters.
Nonlinear Kalman Filters (and Parameter Estimation)
Nonlinear Kalman Filters (and Parameter Estimation)
This course is part of Applied Kalman Filtering Specialization
Instructor: Gregory Plett
Access provided by Abu Dhabi National Oil Company
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Intermediate level
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2 weeks to complete
at 10 hours a week
Flexible schedule
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Assessments
27 assignments
Taught in English
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This course is part of the Applied Kalman Filtering Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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