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)
本课程是 Applied Kalman Filtering 专项课程 的一部分
位教师：Gregory Plett
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26 项作业
授课语言：英语（English）
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本课程是 Applied Kalman Filtering 专项课程 专项课程的一部分
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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.
涵盖的内容
7个视频12篇阅读材料6个作业1个讨论话题
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.
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7个视频7篇阅读材料7个作业3个非评分实验室
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.
涵盖的内容
7个视频7篇阅读材料7个作业3个非评分实验室
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.
涵盖的内容
6个视频6篇阅读材料6个作业2个非评分实验室
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University of Colorado System
University of Colorado System
University of Colorado System
University of Colorado System