During my work at Sensetime, I got myself into a problem that requires clear understanding of Kalman Filter: how does it work, and what do those parameters mean.
After googling around, I found most blogs/answers/projects to be either incomplete or over-complicated. Therefore I decided to write a quick introduction with just enough explanation for a practical Kalman Filter.
This introduction is to share my thoughts and intuitions on the parameters, starting from a simple but excellent example. There is a simple python implementation in the ending section to show how Kalman Filter works as a whole.
I tried to minimize the usage of dredful annotations from linear algebra and probability statistics, thus it shouldn't be too painful to read - though you are required to be with basic knowledge of them. Reading the simple example and my explanation should take less than 10 minutes if you do - have fun reading!