I'm reading the paper at the link below and I was following it for about 2 pages until I hit a road block on the bottom of page 648 where the author says:
putting together 9-11, we obtain
and gives equation 12. I understand equations 9, 10 and 11 but don't see how 12 comes about from them.
Also, if anyone knows a book or a paper that provides an exact mathematical definition of white noise, it's appreciated. Definitely, I'm seeing how important it is to understand white noise, particularly in the context of this paper. Gaussanity ( which is what I'm used to seeing in the many derivations of the Kalman filter ) is clearly quite different ( Dilip, I always believed you but now I'm seeing the importance ) and doesn't cut it here. This paper is actually the first one of a four part-er but, if I can follow this one by the end of 2018, I'd be quite satisfied.