Vaisala RVP900

USER’S MANUAL__________________________________________________________________
208 _________________________________________________________________ M211322EN-D
This filter allows users to specify a narrower nominal filter than the fixed
width case and then when the clutter is strong, this width is extended by the
algorithm (the "hunt"). The interpolation attempts to preserve any
overlapped clutter and weather.
6.2.5.3 GMAP
GMAP is a new adaptive technique developed at Vaisala that is possible
on a high-speed processor such as the RVP900. GMAP has the following
advantages as compared to fixed width frequency domain filters or time
domain filtering such as the IIR approach:
- The width adapts in the frequency domain to adjust for the effects of
PRF, number of samples and the absolute amplitude of the clutter
power. This means that minimal operator intervention is required to
set the filter.
- If there is no clutter present, then GMAP does little or no filtering.
- GMAP repairs the damage to overlapped (near zero velocity) weather
targets.
- The DFT window is determined automatically to be the least
aggressive possible to remove the clutter. This reduces the variance of
the moment estimates.
GMAP Model Assumptions
GMAP makes several assumptions about the model for clutter, weather
and noise, that is,
- The spectrum width of the weather signal is greater than that of the
clutter. This is a fundamental assumption required of all Doppler
clutter filters.
- The Doppler spectrum consists of ground clutter, a single weather
target and noise. Bi-modal weather targets, aircraft or birds mixed
with weather would violate this assumption.
- The width of the clutter is approximately known. This is determined
primarily by the scan speed and to a lesser extent by the climatology
of the local clutter targets. The assumed width is used to determine
how many interior clutter points are removed.
- The shape of the clutter is approximately Gaussian. This shape is used
to calculate how many interior clutter points are removed.
- The shape of the weather is approximately Gaussian. This shape is
used to reconstruct filtered points in overlapped weather.