I find the Kalman filter explanation in terms of the Chokesky decomposition by R. Eubank to be excellent. It has a prerequisite of knowing linear regression (the linear algebra actually know what you are doing type), but made the inference of what the Kalman filter does very clear. This is definitely not a 10 minute blog post explanation, but understanding how it functions on a deep level is worth it.

http://read.pudn.com/downloads158/ebook/707373/A_Kalman_Filt...

I don't know why people jump through hoops to start out with a multidimensional Kalman filter, and even sometimes dive deep into complex topics. The subject seems to suffer from the same terminology and confusion gap as pre-Kolmogorov statistics.