Keep in mind that a Kalman Filter is a method for estimating the state of a linear dynamic system. In practice that means given a state vector $x$, you have to design a Matrix $H$ such that the product $y = Hx$ describes your measurements. That is, $y$ would look like
$ y = \left(\begin{array}{c} latitude \\ longitude \\ \vdots \\ rotation speed \end{array}\right)$
in your case. After you have found a vector $x$ describing your system state and an appropriate $H$, you can then use the Kalman Filter equations to estimate the state $x$ . While have some freedom in choosing $x,y$ and $H$, this still requires some careful thinking.
An alternative would be to use a non-linear estimator. This makes the problem easier in that you only have to find any, arbitrary function $h$ such that $y = h(x)$ is the measurement vector. (This is the same $y$ as above) The most popular non-linear estimator these days is probably the Unscented Kalman Filter, which has better performance than the EKF under almost every scenario.
In any case, I doubt you will find published literature on this exact problem. I had a similar problem 2 years ago, and the most involved systems I could find had about three sensors or less. However, there is Sebastian Thruns free course "How to build a robotic car" on Udacity, which covers a lot of estimation theory, so I would recommend that if you want to learn a bit more.