USER’S MANUAL__________________________________________________________________
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calculated using a rectangular window. If the power in the three
central components is only slightly larger than the noise level, then the
computed width for clutter removal will be so narrow that only the
central (DC) point shall be removed. This is very important since, if
there is no clutter then we want to do nothing or at worst only remove
the central component.
Because of this behaviour, there is no need to do a clutter bypass map,
that is, turn-off the clutter filter at specific ranges, azimuths and
elevation for which the map declares that there is no clutter. Because
of the day-to-day variations in the clutter and the presence of AP, the
clutter map will often be incorrect. Since GMAP determines the no-
filter case automatically and then processes accordingly, a clutter map
is not required.
- Step 4: Replace Clutter Points
The assumption of a Gaussian weather spectrum now comes into play
to replace the points that have been removed by the clutter filter.
There are two cases depending on how the noise level is determined
under Step 2, that is, the dynamic noise case and the fixed noise level
case.
Dynamic noise level case: From Step 2, we know which spectrum
components are noise. From Step 3 we know which spectrum
components are clutter. Presumably, everything that is left is weather
signal. An inverse DFT using only these components is performed to
obtain the autocorrelation at lags 0, 1. This is very computationally
efficient since there are typically few remaining points and only the
first two lags need be calculated. The pulse pair mean velocity and
spectrum width are calculated using the Gaussian model.
2
Note that
since the noise has already been removed, there is no need to do a
noise correction. The Gaussian model is then applied using the
calculated moments to determine a substitution value for each of the
spectrum components that were removed in Step 3.
In the case of overlapped weather as shown in the Figure 43 on page
207 example, the replacement power is typically too small. For this
reason, the algorithm recomputes R0 and R1 using both the observed
and the replacement points and computes new replacement points.
This procedure is done iteratively until the power difference between
two successive iterations is less than 0.2 dB and the velocity
difference is less than 0.5% of the Nyquist interval.
In summary of this step, the Gaussian weather model is used to repair
the filter bias, that is, the damage that is caused by removing the
clutter points. An IIR filtering approach makes no attempt to repair
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