Kalman Filter Boot Camp (and State Estimation)

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Kalman Filter Boot Camp (and State Estimation)

本课程是 Applied Kalman Filtering 专项课程的一部分

位教师：Gregory Plett

3,902 人已注册

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4个模块

深入了解一个主题并学习基础知识。

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2 周完成

在 10 小时一周

灵活的计划

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4个模块

深入了解一个主题并学习基础知识。

24 条评论

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

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您将学习的工具

Matlab

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作业

28 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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该课程共有4个模块

Introduces the Kalman filter as a method that can solve problems related to estimating the hidden internal state of a dynamic system. Develops the background theoretical topics in state-space models and stochastic systems. Presents the steps of the linear Kalman filter and shows how to implement these steps in Octave code and how to evaluate the filter’s output.

This week, you will learn what a Kalman filter is and generally what it does. You will be introduced to the roadmap for the course and the specialization, and will learn some applications that use Kalman filters.

涵盖的内容

6个视频11篇阅读材料6个作业1个讨论话题

6个视频总计84分钟

1.1.1: Welcome to the course!14分钟
1.1.2: What are some key Kalman-filter concepts?16分钟
1.1.3: Working through a Kalman-filter example at a high level16分钟
1.1.4: Roadmap to this course; context within the specialization17分钟
1.1.5: What are some applications that use Kalman filters?18分钟
1.1.6: Summary of "What is the Purpose of a Kalman Filter?" module plus next steps3分钟

11篇阅读材料总计92分钟

Frequently Asked Questions10分钟
Course Resources10分钟
How to Use Discussion Forums10分钟
Earn a Course Certificate10分钟
Are you interested in earning an online MSEE degree?10分钟
Notes for Lesson 1.1.11分钟
Notes for Lesson 1.1.21分钟
Notes for Lesson 1.1.310分钟
Notes for Lesson 1.1.410分钟
Notes for Lesson 1.1.510分钟
Notes for Lesson 1.1.610分钟

6个作业总计80分钟

Graded assignment for week 130分钟
Practice assignment (quiz) for Lesson 1.1.110分钟
Practice assignment (quiz) for Lesson 1.1.210分钟
Practice assignment (quiz) for Lesson 1.1.310分钟
Practice assignment (quiz) for Lesson 1.1.410分钟
Practice assignment (quiz) for Lesson 1.1.510分钟

1个讨论话题总计10分钟

Introduce Yourself10分钟

Kalman filters estimate the "state" of a system that is described using a "state-space model." This week, you will learn the background concepts in state-space models that are required in order to implement a Kalman filter.

涵盖的内容

8个视频9篇阅读材料8个作业2个非评分实验室

8个视频总计183分钟

1.2.1: What is a state-space model and why do I need to know about them?19分钟
1.2.2: Example continuous-time state-space models used for tracking applications24分钟
1.2.3: Understanding the time-domain response of a state-space model26分钟
1.2.4: Illustrating the time-domain response25分钟
1.2.5: Converting continuous-time state-space models to discrete-time27分钟
1.2.6: How do I simulate a discrete-time state-space model?27分钟
1.2.7: Is it even possible for a Kalman filter to estimate this model's state?32分钟
1.2.8: Summary of "What do I need to know about state-space models?" module plus next steps3分钟

9篇阅读材料总计90分钟

Notes for Lesson 1.2.110分钟
Notes for Lesson 1.2.210分钟
Introducing a new element to the course!10分钟
Notes for Lesson 1.2.310分钟
Notes for Lesson 1.2.410分钟
Notes for Lesson 1.2.510分钟
Notes for Lesson 1.2.610分钟
Notes for Lesson 1.2.710分钟
Notes for Lesson 1.2.810分钟

8个作业总计100分钟

Graded assignment for week 230分钟
Practice assignment for Lesson 1.2.110分钟
Practice assignment for Lesson 1.2.210分钟
Practice assignment for Lesson 1.2.310分钟
Practice assignment for Lesson 1.2.410分钟
Practice assignment for Lesson 1.2.510分钟
Practice assignment for Lesson 1.2.610分钟
Practice assignment for Lesson 1.2.710分钟

2个非评分实验室总计30分钟

Jupyter notebook used in conjunction with practice quiz15分钟
Jupyter notebook used in conjunction with practice quiz15分钟

Systems whose state we would like to estimate are affected by unknown inputs ("disturbances" or "process noises") and their measurements are affected by sensor noises. These noises are modeled by random variables. This week, you will learn the background concepts in random variables that are required in order to implement a Kalman filter.

涵盖的内容

8个视频8篇阅读材料8个作业1个非评分实验室

8个视频总计172分钟

1.3.1: Understanding uncertainty via mean and covariance23分钟
1.3.2: Understanding joint uncertainty of two unknown quantities19分钟
1.3.3: Understanding time-varying uncertain quantities23分钟
1.3.4: Simulating correlated Gaussian random vectors28分钟
1.3.5: Discrete-time dynamic systems having random inputs27分钟
1.3.6: Continuous-time dynamic systems having random inputs27分钟
1.3.7: Relating SigmaW to Sw precisely; a little trick (also, relating SigmaV to Sv)21分钟
1.3.8: Summary of "What do I need to know about random variables?" module plus next steps3分钟

8篇阅读材料总计80分钟

Notes for Lesson 1.3.110分钟
Notes for Lesson 1.3.210分钟
Notes for Lesson 1.3.310分钟
Notes for Lesson 1.3.410分钟
Notes for Lesson 1.3.510分钟
Notes for Lesson 1.3.610分钟
Notes for Lesson 1.3.710分钟
Notes for Lesson 1.3.810分钟

8个作业总计100分钟

Graded assignment for week 330分钟
Practice assignment for Lesson 1.3.110分钟
Practice assignment for Lesson 1.3.210分钟
Practice assignment for Lesson 1.3.310分钟
Practice assignment for Lesson 1.3.410分钟
Practice assignment for Lesson 1.3.510分钟
Practice assignment for Lesson 1.3.610分钟
Practice assignment for Lesson 1.3.710分钟

1个非评分实验室总计15分钟

Lab to help computing results for the practice quiz15分钟

Even though we have not yet derived the steps of the Kalman filter, it is instructive to gain insight into a Kalman filter's operation by watching it run. This week, you will learn how to implement a Kalman filter in Octave and see cases where it works well and where it fails (next course, you will learn why!).

涵盖的内容

6个视频6篇阅读材料6个作业4个非评分实验室

6个视频总计69分钟

1.4.1: What are the linear Kalman-filter steps?13分钟
1.4.2: Preparing a model for use with the linear Kalman filter15分钟
1.4.3: How do I implement the Kalman-filter steps in Octave?18分钟
1.4.4: More Kalman-filter examples for state estimation of a linear system13分钟
1.4.5: What can cause a Kalman filter to fail?8分钟
1.4.6: Summary of "State-estimation application of a Kalman filter" module plus next steps3分钟

6篇阅读材料总计60分钟

Notes for Lesson 1.4.110分钟
Notes for Lesson 1.4.210分钟
Notes for Lesson 1.4.310分钟
Notes for Lesson 1.4.410分钟
Notes for Lesson 1.4.510分钟
Notes for Lesson 1.4.610分钟

6个作业总计80分钟

Graded assignment for week 430分钟
Practice assignment for Lesson 1.4.110分钟
Practice assignment for Lesson 1.4.210分钟
Practice assignment for Lesson 1.4.310分钟
Practice assignment for Lesson 1.4.410分钟
Practice assignment for Lesson 1.4.510分钟

4个非评分实验室总计80分钟

Lab to simulate the spring-mass-damper system20分钟
Lab to implement the linear Kalman filter20分钟
Lab to implement open-loop state estimation and demonstrate bad initialization20分钟
Lab to demonstrate some causes for Kalman-filter failure20分钟

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位教师

授课教师评分

(12个评价)

Gregory Plett

University of Colorado System

9 门课程83,964 名学生

提供方

University of Colorado System

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已于 Oct 17, 2025审阅

Very clear explanation of mathematical concepts required for understanding of linear Kalman filters. Thanks!

已于 Mar 10, 2026审阅

Great overview of the basic math elements to understand what the KF does. I would add some programming assignment besides the quizzes to enforce deeper understanding of the concepts.

已于 Mar 29, 2025审阅

Outstanding introduction to Kalman Filtering. A very well designed course. Thanks to Professor Platt.

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