Kalman Filter Tutorial

Always telling this whenever the topic of Kalman Filters come up: If you're learning the Kalman Filter in isolation, you're kind of learning it backwards and missing out on huge "aha" moments that the surrounding theory can unlock. To truly understand the Kalman Filter, you need to study Least Squares (aka linear regression), then recursive Least Squares, then the Information Filter (which is a different formulation of the KF). Then you'll realize the KF is just recursive Least Squares reformulated…

I always get excited when I see these "tutorials for dummies" (like me). Like "finally, I get to take an evening to understand this concept that's eluded me for years." Generally, I get let down. This time is no exception. They always start off well, then inevitably there's a concept or key terminology that gets glossed over without sufficient explanation. . "The random variable is described by the probability density function. The probability density function is characterized by moments. The…