Im Jang-hwan's Kalman Filter 1

The difficult and challenging Kalman filter,
Let's understand it clearly with examples ✨

Studying with examples
How the Kalman Filter Works 💡

The Kalman filter is a mathematically complex algorithm, making it quite challenging to understand. Learning it requires a significant foundation in linear algebra, probability theory, and statistics. It's not easy! I've been there myself. I've hit a wall several times while studying. Despite the numerous lectures available, I still haven't fully grasped the Kalman filter. It's impossible to explain every Kalman filter, and there's no need to.

So, I decided to explain how the Kalman Filter works with a few simple examples . Once you understand the Kalman Filter, you can apply it to your field of expertise. Is it better to apply it without understanding the theory, or to apply it with knowledge of the theory and principles? The wise choice is yours.

Lecture Features ✨

In this lecture, I'll explain the working principles of the Kalman filter in a very concrete way, using simple examples to facilitate as intuitive a understanding as possible. If you utilize this lecture effectively, I believe it will significantly reduce the time it takes to understand the Kalman filter.

I recommend this to these people 🙆‍♀️

Anyone who knows a little about what a Kalman filter is

Control engineering, robotics, signal processing, and computer vision majors

Graduate students who want to learn the Kalman filter thoroughly

What you'll learn 📚

Expected Questions Q&A 💬

Please check before taking the class 📢

• I've implemented the theoretical content primarily in Python. I've also included some MatLab programs for my own needs. I'll upload the programs I implemented, but I want to emphasize that each student is responsible for implementing them.
  • For reference, I first programmed using easy Matlab and then using PyCharm.
  • The purpose of this lecture is to focus on the theoretical explanation of the Kalman Filter. Therefore, implementation is the responsibility of each student.
• Class materials are uploaded in PDF format, and program files are uploaded in text format.

Introducing the Knowledge Sharer ✒️

• Current 3D Computer Vision Researcher
• Current YouTube Channel Operator: Lim Jang-hwan: 3D Computer Vision
• Current) Face book: SLAM KR Group (Mathematics Expert Committee)
• Former) Doctor of Science (topology) from Kile University, Germany
• Former Research Professor, Graduate School of Advanced Imaging Science, Chung-Ang University (3D Computer Vision Research)
• Book: Optimization Theory