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 Martin Luther Christian University
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
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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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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
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