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)
Taught in English
Intermediate level
Recommended experience
21 hours to complete
3 weeks at 7 hours a week
Flexible schedule
Learn at your own pace
Skills you'll gain
Details to know
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Assessments
27 assignments
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There are 4 modules in this course
This week, you will learn how to implement the extended Kalman filter to estimate the state of a nonlinear system.
What's included
8 videos13 readings7 assignments1 discussion prompt1 ungraded lab
This week, you will learn how to implement the sigma-point Kalman filter to estimate the state of a nonlinear system.
What's included
6 videos6 readings6 assignments1 ungraded lab
This week, you will learn how to extend and refine nonlinear Kalman filters for special cases.
What's included
7 videos7 readings7 assignments3 ungraded labs
This week, you will learn how to use nonlinear Kalman filters to estimate model parameter values.
What's included
7 videos7 readings7 assignments3 ungraded labs
Instructor
Offered by
Recommended if you're interested in Electrical Engineering
University of Colorado System
University of Colorado System
University of Colorado System
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