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Kalman Filter Boot Camp (and State Estimation)

Ce cours n'est pas disponible en Français (France)

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Kalman Filter Boot Camp (and State Estimation)

Ce cours fait partie de Spécialisation "Applied Kalman Filtering"

Instructeur : Gregory Plett

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4 modules

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Expérience recommandée

2 semaines à compléter

à 10 heures par semaine

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Apprenez à votre propre rythme

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

25 avis

niveau Intermédiaire

Expérience recommandée

2 semaines à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Compétences que vous acquerrez

Catégorie : Probability Distribution
Catégorie : Linear Algebra
Catégorie : Probability & Statistics
Catégorie : Estimation
Catégorie : Simulations
Catégorie : Mathematical Modeling
Catégorie : Probability
Catégorie : Simulation and Simulation Software
Catégorie : Statistical Methods
Catégorie : Statistical Modeling
Catégorie : Control Systems

Outils que vous découvrirez

Catégorie : Matlab

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28 devoirs

Enseigné en Anglais

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Ce cours fait partie de la Spécialisation "Applied Kalman Filtering"

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Il y a 4 modules dans ce cours

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.

Détails du module

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.

Inclus

6 vidéos11 lectures6 devoirs1 sujet de discussion

6 vidéosTotal 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 lecturesTotal 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 devoirsTotal 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 sujet de discussionTotal 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.

Inclus

8 vidéos9 lectures8 devoirs2 laboratoires non notés

8 vidéosTotal 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 lecturesTotal 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 devoirsTotal 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 laboratoires non notésTotal 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.

Inclus

8 vidéos8 lectures8 devoirs1 laboratoire non noté

8 vidéosTotal 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 lecturesTotal 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 devoirsTotal 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 laboratoire non notéTotal 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!).

Inclus

6 vidéos6 lectures6 devoirs4 laboratoires non notés

6 vidéosTotal 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 lecturesTotal 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 devoirsTotal 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 laboratoires non notésTotal 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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Instructeur

Évaluations de l’enseignant

(12 évaluations)

Gregory Plett

University of Colorado System

9 Cours84 797 apprenants

Offert par

University of Colorado System

Avis des étudiants

5 stars
92 %
4 stars
8 %
3 stars
0 %
2 stars
0 %
1 star
0 %

Affichage de 3 sur 25

Révisé le 17 oct. 2025

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

Révisé le 10 mars 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.

Révisé le 22 mai 2026

Great course, but it is advanced. You need a decent math background: calculus, linear algebra, probability and somewhat of differential equations.

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