If you just want to implement Kalman Filtering, and by this mean 'coding it up' and already have the coding skills, nothing really other than a good reference book. Personally, I learned Kalman Filtering from Hamilton, Time Series Analysis; and from Oksendahl, Stochastic Differential Equations; and this gives an idea which direction to look to get a better, true understanding of the Filter. Most importantly, learning some probability theory would certainly help as it's usually cast in that framework. It doesn't have to be measure-theoretic, although it certainly helps if you understand probability on that level. Some probability books I liked and learned from were Williams, Probability with Martingales (also does some filtering), and Ross, Stochastic Processes.
It all depends on how deeply you want to get into it. Don't forget that learning by doing is often the best medicine (and one that I tend to forget): so just grab a reference, code up what you want to use it for, and play with it. Gl! (P.S.: None of the books I mentioned is specifically focused on KF. There are certainly books for that too, but I lack familiarity).