As a follow-on course to "Kalman Filter Boot Camp", this course derives the steps of the linear Kalman filter to give understanding regarding how to adjust the method to applications that violate the standard assumptions. Applies this understanding to enhancing the robustness of the filter and to extend to applications including prediction and smoothing. Shows how to implement a target-tracking application in Octave code using an interacting multiple-model Kalman filter.
Linear Kalman Filter Deep Dive (and Target Tracking)
Linear Kalman Filter Deep Dive (and Target Tracking)
This course is part of Applied Kalman Filtering Specialization
Instructor: Gregory Plett
Included with 
Gain insight into a topic and learn the fundamentals.
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
- Describing the role of all Kalman-filter variables
- Understanding purpose of sequential-probabilistic-inference steps
- Modifying Kalman-filter steps to enable applications that violate the standard assumptions
- Modifying Kalman-filter steps to make it more robust
- Implement a Kalman filter for a target-tracking application
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Recently updated!
September 2024
Assessments
26 assignments
Taught in English
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This course is part of the Applied Kalman Filtering Specialization
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There are 4 modules in this course
Knowing how to derive the steps of the Kalman filter is important for understanding the assumptions that are made and to be able to re-derive the steps for different assumptions. This week, you will learn how to derive the steps and will gain insight into how the Kalman filter works.
What's included
7 videos12 readings6 assignments1 discussion prompt
Last week, you learned the assumptions made when deriving the Kalman filter. What if these assumptions are not met correctly? What if numeric roundoff error causes failure? This week, you will learn how to solve problems with the standard Kalman filter.
What's included
7 videos7 readings7 assignments3 ungraded labs
The standard linear Kalman filter works well for state estimation, but can be extended to implement prediction and smoothing as well. Further, we can speed up the steps or even eliminate steps in some circumstances. This week, you will learn some extensions and refinements to linear Kalman filters.
What's included
7 videos7 readings7 assignments3 ungraded labs
A popular application of Kalman filters is to track (usually non-cooperating) targets. This week, you will learn how to implement standard and specialized Kalman filters suited for target tracking.
What's included
6 videos6 readings6 assignments2 ungraded labs
Instructor
Offered by
Recommended if you're interested in Electrical Engineering
MathWorks
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Korea Advanced Institute of Science and Technology(KAIST)
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