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Kalman Filter Boot Camp (and State Estimation)

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Kalman Filter Boot Camp (and State Estimation)

Dieser Kurs ist Teil von Spezialisierung „Applied Kalman Filtering“

Dozent: Gregory Plett

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4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

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2 Wochen zu vervollständigen

unter 10 Stunden pro Woche

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4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

25 Bewertungen

Stufe Mittel

Empfohlene Erfahrung

2 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Kompetenzen, die Sie erwerben

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

Werkzeuge, die Sie lernen werden

Kategorie: Matlab

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Dieser Kurs ist Teil der Spezialisierung Spezialisierung „Applied Kalman Filtering“

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In diesem Kurs gibt es 4 Module

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.

Moduldetails

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.

Das ist alles enthalten

6 Videos11 Lektüren6 Aufgaben1 Diskussionsthema

6 VideosInsgesamt 84 Minuten

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

11 LektürenInsgesamt 92 Minuten

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

6 AufgabenInsgesamt 80 Minuten

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

1 DiskussionsthemaInsgesamt 10 Minuten

Introduce Yourself10 Minuten

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.

Das ist alles enthalten

8 Videos9 Lektüren8 Aufgaben2 Unbewertete Labore

8 VideosInsgesamt 183 Minuten

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

9 LektürenInsgesamt 90 Minuten

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

8 AufgabenInsgesamt 100 Minuten

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

2 Unbewertete LaboreInsgesamt 30 Minuten

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

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.

Das ist alles enthalten

8 Videos8 Lektüren8 Aufgaben1 Unbewertetes Labor

8 VideosInsgesamt 172 Minuten

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

8 LektürenInsgesamt 80 Minuten

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

8 AufgabenInsgesamt 100 Minuten

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

1 Unbewertetes LaborInsgesamt 15 Minuten

Lab to help computing results for the practice quiz15 Minuten

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!).

Das ist alles enthalten

6 Videos6 Lektüren6 Aufgaben4 Unbewertete Labore

6 VideosInsgesamt 69 Minuten

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

6 LektürenInsgesamt 60 Minuten

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

6 AufgabenInsgesamt 80 Minuten

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

4 Unbewertete LaboreInsgesamt 80 Minuten

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

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Dozent

Lehrkraftbewertungen

(12 Bewertungen)

Gregory Plett

University of Colorado System

9 Kurse84.750 Lernende

von

University of Colorado System

Bewertungen von Lernenden

5 stars
92 %
4 stars
8 %
3 stars
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2 stars
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Geprüft am 17. Okt. 2025

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

Geprüft am 29. März 2025

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

Geprüft am 10. März 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.

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