Kalman Filter Boot Camp (and State Estimation)

Kalman Filter Boot Camp (and State Estimation)

This course is part of Applied Kalman Filtering Specialization

Instructor: Gregory Plett

Access provided by EDGE Group

3,374 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

23 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.

23 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

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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

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.

What's included

6 videos11 readings6 assignments1 discussion prompt

6 videos Total 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 level 16 minutes
1.1.4: Roadmap to this course; context within the specialization 17 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 steps 3 minutes

11 readings Total 92 minutes

Frequently Asked Questions 10 minutes
Course Resources 10 minutes
How to Use Discussion Forums 10 minutes
Earn a Course Certificate 10 minutes
Are you interested in earning an online MSEE degree? 10 minutes
Notes for Lesson 1.1.1 1 minute
Notes for Lesson 1.1.2 1 minute
Notes for Lesson 1.1.3 10 minutes
Notes for Lesson 1.1.4 10 minutes
Notes for Lesson 1.1.5 10 minutes
Notes for Lesson 1.1.6 10 minutes

6 assignments Total 80 minutes

Practice assignment (quiz) for Lesson 1.1.1 10 minutes
Practice assignment (quiz) for Lesson 1.1.2 10 minutes
Practice assignment (quiz) for Lesson 1.1.3 10 minutes
Practice assignment (quiz) for Lesson 1.1.4 10 minutes
Practice assignment (quiz) for Lesson 1.1.5 10 minutes
Graded assignment for week 1 30 minutes

1 discussion prompt Total 10 minutes

Introduce Yourself 10 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 videos Total 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 applications 24 minutes
1.2.3: Understanding the time-domain response of a state-space model 26 minutes
1.2.4: Illustrating the time-domain response 25 minutes
1.2.5: Converting continuous-time state-space models to discrete-time 27 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 steps 3 minutes

9 readings Total 90 minutes

Notes for Lesson 1.2.1 10 minutes
Notes for Lesson 1.2.2 10 minutes
Introducing a new element to the course! 10 minutes
Notes for Lesson 1.2.3 10 minutes
Notes for Lesson 1.2.4 10 minutes
Notes for Lesson 1.2.5 10 minutes
Notes for Lesson 1.2.6 10 minutes
Notes for Lesson 1.2.7 10 minutes
Notes for Lesson 1.2.8 10 minutes

8 assignments Total 100 minutes

Practice assignment for Lesson 1.2.1 10 minutes
Practice assignment for Lesson 1.2.2 10 minutes
Practice assignment for Lesson 1.2.3 10 minutes
Practice assignment for Lesson 1.2.4 10 minutes
Practice assignment for Lesson 1.2.5 10 minutes
Practice assignment for Lesson 1.2.6 10 minutes
Practice assignment for Lesson 1.2.7 10 minutes
Graded assignment for week 2 30 minutes

2 ungraded labs Total 30 minutes

Jupyter notebook used in conjunction with practice quiz 15 minutes
Jupyter notebook used in conjunction with practice quiz 15 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 videos Total 172 minutes

1.3.1: Understanding uncertainty via mean and covariance 23 minutes
1.3.2: Understanding joint uncertainty of two unknown quantities 19 minutes
1.3.3: Understanding time-varying uncertain quantities 23 minutes
1.3.4: Simulating correlated Gaussian random vectors 28 minutes
1.3.5: Discrete-time dynamic systems having random inputs 27 minutes
1.3.6: Continuous-time dynamic systems having random inputs 27 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 steps 3 minutes

8 readings Total 80 minutes

Notes for Lesson 1.3.1 10 minutes
Notes for Lesson 1.3.2 10 minutes
Notes for Lesson 1.3.3 10 minutes
Notes for Lesson 1.3.4 10 minutes
Notes for Lesson 1.3.5 10 minutes
Notes for Lesson 1.3.6 10 minutes
Notes for Lesson 1.3.7 10 minutes
Notes for Lesson 1.3.8 10 minutes

8 assignments Total 100 minutes

Practice assignment for Lesson 1.3.1 10 minutes
Practice assignment for Lesson 1.3.2 10 minutes
Practice assignment for Lesson 1.3.3 10 minutes
Practice assignment for Lesson 1.3.4 10 minutes
Practice assignment for Lesson 1.3.5 10 minutes
Practice assignment for Lesson 1.3.6 10 minutes
Practice assignment for Lesson 1.3.7 10 minutes
Graded assignment for week 3 30 minutes

1 ungraded lab Total 15 minutes

Lab to help computing results for the practice quiz 15 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 videos Total 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 filter 15 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 system 13 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 steps 3 minutes

6 readings Total 60 minutes

Notes for Lesson 1.4.1 10 minutes
Notes for Lesson 1.4.2 10 minutes
Notes for Lesson 1.4.3 10 minutes
Notes for Lesson 1.4.4 10 minutes
Notes for Lesson 1.4.5 10 minutes
Notes for Lesson 1.4.6 10 minutes

6 assignments Total 80 minutes

Practice assignment for Lesson 1.4.1 10 minutes
Practice assignment for Lesson 1.4.2 10 minutes
Practice assignment for Lesson 1.4.3 10 minutes
Practice assignment for Lesson 1.4.4 10 minutes
Practice assignment for Lesson 1.4.5 10 minutes
Graded assignment for week 4 30 minutes

4 ungraded labs Total 80 minutes

Lab to simulate the spring-mass-damper system 20 minutes
Lab to implement the linear Kalman filter 20 minutes
Lab to implement open-loop state estimation and demonstrate bad initialization 20 minutes
Lab to demonstrate some causes for Kalman-filter failure 20 minutes