ne of
the greatest theories postulated in the twentieth century is the Kalman Filter.
It is not actually a filter, but a mathematical estimator, which uses a series
of measurements observed over a period of time consisting of random variations
( noise) and other inaccuracies and produces estimates that tend to be more
precise than what would be based on a single measurement.
The
Kalman filter has numerous applications in technology. A common application is
for guidance, navigation and control of vehicles, particularly aircraft and
spacecraft.
Take for example the readings from a GPS receiver or the outputs of an
IRS. The outputs are not always predictable and contain some errors which may
be classified as random and systemic. The simplest solution that comes to mind
is to take average of a series of consequent samples. This simple approach
doesn’t work for most problems. We need a more sophisticated approach.
