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Kalman Filter Boot Camp (and State Estimation)

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Kalman Filter Boot Camp (and State Estimation)

This course is part of Applied Kalman Filtering Specialization

Instructor: Gregory Plett

4,098 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

24 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

24 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Matlab

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

28 assignments

Taught in English

91%

of learners achieved a positive career outcome

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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

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.

Module details

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.

What's included

6 videos11 readings6 assignments1 discussion prompt

6 videosTotal 84 minutes

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

11 readingsTotal 92 minutes

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

6 assignmentsTotal 80 minutes

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

1 discussion promptTotal 10 minutes

Introduce Yourself10 minutes

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.

What's included

8 videos9 readings8 assignments2 ungraded labs

8 videosTotal 183 minutes

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

9 readingsTotal 90 minutes

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

8 assignmentsTotal 100 minutes

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

2 ungraded labsTotal 30 minutes

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

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.

What's included

8 videos8 readings8 assignments1 ungraded lab

8 videosTotal 172 minutes

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

8 readingsTotal 80 minutes

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

8 assignmentsTotal 100 minutes

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

1 ungraded labTotal 15 minutes

Lab to help computing results for the practice quiz15 minutes

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!).

What's included

6 videos6 readings6 assignments4 ungraded labs

6 videosTotal 69 minutes

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

6 readingsTotal 60 minutes

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

6 assignmentsTotal 80 minutes

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

4 ungraded labsTotal 80 minutes

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

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Instructor

Instructor ratings

(12 ratings)

Gregory Plett

University of Colorado System

9 Courses84,721 learners

Offered by

University of Colorado System

Learner reviews

5 stars
91.66%
4 stars
8.33%
3 stars
0%
2 stars
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Showing 3 of 24

Reviewed on Oct 17, 2025

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

Reviewed on Mar 10, 2026

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.

Reviewed on Mar 29, 2025

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

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