Abstract
Inversion of linear systems of equations is straightforward, although there are
some complications when the equations represent a physical process and there is
noise in the measurements of the dependent variables. This paper extends the
linear inversion method to systems of quadratic equations. Using this method,
we can devise better inversion algorithms, and better estimates of the error in
the inversion process, by including quadratic terms explicitly. I examine two
different ways that quadratic terms can be included; which method we use will
depend largely on the relative sizes of the various non-linear terms. This
method will also greatly improve our ability to evaluate the potential
performance of proposed sensor systems.
This was published in the International Journal of Remote Sensing. Request a Copy of the Article.