With Kalman filtering, the term filter assumed a meaning that is well beyond the
original idea of separation of the components of a mixture. It has also come to
include the solution of an inversion problem, in which one knows how to represent
the measurable variables as functions of the variables of principal interest. In essence,
it inverts this functional relationship and estimates the independent variables as
inverted functions of the dependent (measurable) variables. These variables of interest
are also allowed to be dynamic, with dynamics that are only partially predictable