Chapter 6 ______________________________________________________ Processing Algorithms
VAISALA______________________________________________________________________ 211
there is always a finite probability that a few components will have
extremely large values.
There are generally two regions: a noise region on the left (weaker
power) and a signal/clutter region on the right (stronger power). The
noise level and the transition between these two regions is determined
by first summing the power in the range 5% to 40%. This sum is used
to determine the noise level by comparing with the sum value
corresponding to the theoretical curve. Next, the power is summed
beyond the 40% point for both the actual and theoretical rank spectra.
The point where the actual power sum exceeds the theoretical value
by 2 dB determines the boundary between the noise region and the
signal/clutter region.
Finally there are two outputs from this step: a spectrum noise level and
a list of components that are either signal or clutter
- Step 3: Remove the Clutter Points
The inputs for this step are the Doppler power spectrum, the assumed
clutter width in m/s and the noise level, either known from noise
measurement or optionally calculated from the previous step. First the
power in the three central spectrum components is summed (DC ±1
component) and compared to the power that would be in the three
central components of a normalized Gaussian spectrum having the
specified clutter width and discretized in the identical manner. This
serves as a basis for normalizing the power in the Gaussian to the
observed power. The Gaussian is extended down to the noise level and
all spectral components that fall within the Gaussian curve are
removed. The power in the components that are removed is the
"clutter power".
A subtle point is the use of the three central points to do the power
normalization of the actual vs the idealized spectrum of clutter. This
is more robust than using a single point since for some realizations of
clutter targets viewed with a scanning antenna, the DC component is
not necessarily the maximum. Averaging over the three central
components is a more robust way to characterize the clutter power.
The very substantial algorithmic work that has been done thus far is to
eliminate the proper number of central points. The operator only has
to specify a nominal clutter width in m/s. This means that the operator
does not need to consider the PRF, wavelength or number of spectrum
points- GMAP accounts for these automatically.
A key point is that in the event that the sum of the three central
components is less than the corresponding noise power, then it is
assumed that there is no clutter and all of the moments are then
To Next Page
Loading...