Pretty happy with it now. Needs copy editing, and probably an
easier introduction to convey the basic idea. Moved from a class
based approach to a procedural approach, and I like that very much.
I made a lot of changes so that each chapter makes clear that
they are all implementing the same basic bayesian algorithm.
This required a lot of editting, and it doesn't make sense to
try to do that atomically, hence this huge check in.
I made a lot of edits, and haven't copy editted anything. i'm
sure I introduced a lot of problems and discontinuities.
Still needs work; code is messy, and Q is not computed. But I am
getting correct results.
Also need to expand explanations and scenarios.
Need to remove now redundant explanations in EKF chapter, since
they exist in the UKF chapter.
Most of the text is wrong, but changed code to use the
renamed ScaledUnscentedKalmanFilter.
Checking in with bad text because I am in the process of changing
FilterPy to use a class for the sigma points to make it easier
to change the sigma point generation, leaving us with one
UKF class instead of several.
Let this check in get away from me. Bunch of small formatting sttuf
in the various chapters, some experimental animations and code, etc.
Not a clearly delineated check in.
Most of the graphs were really too tall. Well, we can quibble about that
but I made them smaller to reduce the PDF size and thus print size
of the full book.
I think I finally arrived at a good ordering of material.
Started with implementing a linear problem just so we can
see how it differs from the linear KF, then added problems
step by step. Got rid of most of the poor performing filters.
