2.6.1.2.4.2 Descalloping
The purpose of this section is to
discuss the problem known as
scalloping, and the process used to
correct it, known as descalloping.
2.6.1.2.4.2.1 Scalloping A radar system can
be operated in burst mode
such that the sensor is on for a
period of time, then off, then on
again, and so on, effectively
imaging the region of interest in a
series of bursts, where
each burst consists of a certain
number of echoes. In comparison with
the conventional stripmode imaging
methods, burst mode operation
enables a reduced power consumption
and data rate at the expense of the
resolution and image quality
achieved. Since a sensor
operating in burst mode does not
view a scene continuously,
achieving uniform image quality
over the imaged extent is
more difficult for a burst mode
imaging system than for a
continuous mode one. In
particular, burst mode images
are subject to cyclic
radiometric banding effects as
described below.
During azimuth
compression, the data in
each burst must be adjusted by
the proper azimuth gain
correction function. This is due
to the fact that the return
energy from a single scatterer
is modulated in azimuth
according to that portion of the
antenna gain pattern used in the
burst imaging event, an
effect known as scalloping
Ref. [2.11 ]
(see figure2.34 Below).
In other words, different point
targets are illuminated by
different positions of the
azimuth gain pattern yielding
different integrated powers. The
amount of scalloping
corresponds to the energy
difference from the beginning to
the end of the processing
bandwidth and is generally
measured in decibels (dB).

Figure 2.34 Return Energy From a Single Scatterer (3 bursts shown) 
One method to correct
for azimuth scalloping across
individual bursts involves applying
descalloping functions
which are inversely proportional
to the predicted antenna gain
pattern function, thus resulting in
a constant magnitude in azimuth
across the corrected burst, as
shown in figure2.35 a. In this
figure, the uncompensated burst
energy is drawn as the solid line,
and the descalloping (correction)
function is drawn as the dotted
line. Consequentially, the
radiometry is rendered flat in
azimuth along the full image extent
(figure2.35 b).
However, an inaccurate
estimation of the fractional Doppler
centroid frequency 2.6.1.2.2.
leads to a misapplication of
each of the azimuth descalloping
functions to its corresponding
burst image return signal. As a
result, the output signal level
is rendered nonconstant across
the azimuth burst as shown in figure2.36 a
below. In this figure, the
Doppler centroid error ( f
_{Derror} ) causes a
cyclic rotation in the
uncompensated burst energy
function along azimuth (as shown
in the solid line), resulting in
an incorrect application of the
descalloping function (shown as
the dotted line).This
radiometric variation in
azimuth across each burst
results in a cyclic radiometric
pattern known as residual
scalloping across the full
image in azimuth, as
depicted in figure2.36 b.

Figure 2.35 The application of a suitable correction function to an azimuth burst with an accurate Doppler centroid frequency estimate 

Figure 2.36 The application of a suitable correction function to an azimuth burst in the presence of a Doppler centroid frequency estimation error 
2.6.1.2.4.2.2 Methods for Scalloping Correction
2.6.1.2.4.2.2.1 Azimuth Descalloping
Function Application There are two
possible methods that may be
selected for calculating the
descalloping function: the
Inverse Beam Pattern Method and
the Constant SNR method.
Inverse Beam Pattern
Descalloping
Method One method
to correct for scalloping
across azimuth involves
applying weighting
functions which are
inversely proportional to
the azimuth antenna gain
function. This technique
shall be referred to as the
Inverse Beam Pattern Method
for descalloping.
To apply the correction
effectively, the Doppler
centroid frequency must be
known accurately.
The weighting functions
derived using the Inverse
Beam Pattern method
emphasise the outer looks to
equalise the power, thereby
maximising the equivalent
number of looks, or speckle
reduction, over azimuth.
Constant
signaltonoise ratio
(SNR) Descalloping
Method
Bamler's "Optimum
Look Weighting for
BurstMode and ScanSAR
Processing" (
Ref. [2.11 ]
) has
developed a set of
antenna pattern correction
functions for burst mode and
ScanSAR processing. The
functions are derived to
satisfy the following
criteria in the
multilook azimuth
processing case:
 The image signal energy
becomes constant over azimuth,
 The noise energy becomes
constant over azimuth, and
 The equivalent number of
looks (which describes
the amount of speckle
reduction obtained by a
weighted multilook
process) is maximised
over azimuth.
The first and second
criteria results in a constant
signaltonoise ratio (SNR) over
azimuth. By allowing a variation
in the signal image level in
the derivation of the constant
SNR weighting functions, the
sensitivity of residual
scalloping to Doppler centroid
frequency estimation error
is reduced. Note that in the
case of only two azimuth looks,
the signal image level is fixed
as there are no degrees of
freedom available to maximise.
In addition, the constant
SNR method generates
weighting functions which
deemphasise the outer
looks, so that the
equivalent number of
looks is not maximised.
2.6.1.2.4.2.2.2 Doppler Centroid
Frequency Estimation in the
context of Burst Mode Data An accurate estimate of
the Doppler centroid
frequency 2.6.1.2.2. is necessary to
reduce the effect of residual
scalloping, as shown in figure.2.36
For both the Inverse
Beam Pattern method and the
Constant SNR method, the
sensitivity to Doppler centroid
errors decreases as the number
of looks increases.
2.6.1.2.4.2.3 Notes
An approximation in
the ASAR implementation of this
method is described as follows. The
correction at each azimuth location
is proportional to the inverse of
the midpoint of the azimuth
beam pattern over the portion of
the beam pattern used in focussing
the image at that azimuth location.
This is assumed to approximate, at
each azimuth location, the
inverse of the integral of
the azimuth beam pattern over the
portion of the beam pattern used in
focussing the image at that
azimuth location.
2.6.1.2.4.2.4 References
Bamler R,
"Optimum Look Weighting for
BurstMode and ScanSAR
Processing", IEEE
Transactions on Geoscience and
Remote Sensing Vol. 33 No. # pp.
722725, May 1995.
2.6.1.2.4.3 ScanSAR Beam Merging
ScanSAR multibeam
processing consists of generating
independent beam images (called
beam buffers) and subsequently
combining them into a single output
line, as shown in figure2.37 below:

Figure 2.37 Merging Beam Buffers 
The beam buffers overlap in
range so, in general, the data should be
merged to allow the output image line to
be continuous. Data in the
merged line is either copied
directly from one beam, or the
merged line data is a blend of pixels from the so
called blend region of the two beams.
Therefore, the beam merging algorithm
consists of three primary steps
described ago.
2.6.1.2.4.3.1 Determine a reference
point in the blend region The reference point of
the blend region is defined as the
pixel corresponding to the range at
which the elevation beam patterns
for near and far beam intersect.
Therefore, the blend region
necessarily falls inside the overlap
region, which is the region defined
by the first pixel of the far
beam and the last pixel of the near
beam, as indicated in the figure2.38 below.
2.6.1.2.4.3.2 Determine the limits of
the blend region
The nominal size of the blend
region, say N (even number), is
specified in a PFASAR input
parameter file. A number of N/2
points are extracted on
either side of the reference
point for each (near and far)
beam, so the reference point is
precisely the centre of the
blend region. If necessary,
N will be clipped against the
limits of the overlap region.

Figure 2.38 The Blend Region and its Reference Point 
2.6.1.2.4.3.3 Merge the data in the two
beams corresponding to the blend
region according to a predefined rule The merging formula
used to complete this third step is
designed so that the signaltonoise
ratio of the imagery of the merged
data tends to be maximised.
In describing the method, the
following notation is used:
xnear(n) = the array of near beam
pixel power values;
xfar (n) = the array of far beam
pixel power values and;
p = weight rate
For a given value of p, the
formula used to calculate the
merged pixels is given by:
xmerged(n) = (1  (n/N)p) *
xnear(n) + ((n/N)p) * xfar(n),
where n =0,1,...,N1 and p >=
0. The data on the steeper
extremities of the beam pattern,
where the effects of a bias in
the elevation beam pattern
correction are more significant,
are disregarded while the data
on the steeper extremities of
the blend region are
weighted less by the merging
model. This is because the
following continuity conditions
hold true:
xmerged(0) = xnear(0)
xmerged(N) = xfar (N)
In particular, if xfar(n) =
xnear(n) for all n =
0,1,...,N1, then
xmerged(n) = xfar(n) = xnear(n)
for all n = 0,1,...,N1
The desired merging scenario can
be easily selected by simply
setting the weight rate
parameter, p, as shown in the
following table:

Table 2.33 Setting Weight Rates Parameter

Parameter p 
Weighting 
p = 1 
current weighting scenario 
p > 1 
the near beam weighs
more(it is favoured) 
0 < p < 1 
the far beam weighs
more(it is favoured) 
p = 0 
only the far beam
contributes to the
merged one 
p = infinity 
only the near beam
contributes to the
merged one 
2.6.1.2.4.4 Global Monitoring Mode
Inverse Filter
The transmitted pulse typically
has a rectangular amplitude, and a
quadratic phase variation that gives
the pulse its linear FM character. For
linear FM pulses, increasing the
duration of the pulse allows greater
frequency
variation across the pulse, thus
increasing the pulse bandwidth and
resulting in a higher resolution
after pulse
compression. (See the
description of range compression in
the section on RangeDoppler 2.6.1.2.3.1.2.
processing). Linear FM pulses that
are used for pulse compression
typically have a large
timebandwidth product, at least 100.
For linear FM pulses with a large
timebandwidth product, there is a
timefrequency relationship, such
that the amplitude spectrum has
the same shape as the pulse
amplitude in the time domain. For
example, the figure below shows the
amplitude of the Fourier transform
(FFT) of a large timebandwidth
product pulse with a rectangular shape.

Figure 2.39 Pulse spectrum with large timebandwidth product. 
The Fourier transform
(FFT) also has a quadratic phase
variation that needs to be
removed during matched
filtering, in order to compress
the pulse. In the frequency
domain, the matched filter is
defined as the conjugate of the
Fourier transform of the pulse. For
large timebandwidth product
pulses, multiplication of the pulse
spectrum by its conjugate removes
the quadratic phase variation, and
leaves the amplitude approximately
unchanged because of the rectangular
shape, resulting in the
sinc shaped impulse response.
However, in Global Monitoring
(GM) Mode where the required
resolution is quite low, a
relatively short pulse with smaller
bandwidth is used.
In this case the timebandwidth
product is quite low, on the order
of 20, and the timefrequency
relationship does not hold as well.
For example, the figure below
shows the amplitude of the Fourier
transform of a pulse used in Global
Monitoring Mode. The amplitude is no
longer rectangular, and
multiplication by the matched filter
would worsen the amplitude
variation, which affects the shape
of the compressed pulse.
full size

Figure 2.40 Pulse spectrum with small timebandwidth product. 
To solve the problem caused by the
shape of the amplitude spectrum when
the time bandwidth product is low,
the variation of the amplitude
spectrum is removed by multiplying
the pulse spectrum by the inverse of the
Fourier transform (IFFT)of
the pulse, rather than the
conjugate. This removes the
amplitude variation of the spectrum
during pulse compression,
improving the shape of the
compressed pulse.
2.6.1.2.5 ASAR Strip Line Product Processing
2.6.1.2.5.1 2.6.1.2.5.1 Introduction
The ASAR Instrument may
be operated in one high rate mode
continuously (an acquisition
segment) for up to ten minutes
duration or for up to one orbit
duration in a lowrate mode called
Global
Monitoring (GM) mode. Stripline
products contain image data from an
entire acquisition segment, up to a
maximum of 10 minutes per product
for IM, AP, and WS and up to a
full orbit (100 minutes) for GM. All
mediumresolution and browse
products (IMM, IMB, APM, APB,
WSM, WSB, GM1, and GMB) are
processed as stripline products.
The browse products are subsampled
versions of the mediumresolution
products and are processed at the
same time. ( For a further
discussion of all of these products
refer to the section entitled "Level 1B
Image Products" 2.6.2.1.1.3. and "Browse
Products" 2.6.2.1.3. , which can both
be found in chapter 2 ).
The PFASAR will systematically
generate output parent products,
within the time constraints, that
cover an entire acquisition segment
by processing a number of separate
slices and then joining the slices
to form the continuous segment. Most
long data segments will be
processed using more than one
processing computer, in order to
meet throughput performance
requirements. Each computer will be
given a portion of the input
data segment to process. The output,
referred to as slices, from the
different computers are then
concatenated to produce one long
strip product, referred to as
stripline product.
This stripline parent product is
stored as a single product in the
Archive Facility (ARF) and key
information about the product is
maintained in the inventory database
for access by users. After the
segment product is archived, a
customer may extract a subset, or child product.
If a customer orders a floating scene
from within the stripline product
the appropriate portion of the
archived product, i.e. the measurement
data, annotation
data, Specific Product
Header (SPH) and Main Product
Header (MPH), will be extracted
to form the requested output
product. The extracted subset
product may start at any point in
the segment and shall not contain
any radiometric or geometric discontinuities.
Geometric continuity is achieved by
ensuring all PFASAR computers
establish the same output grid.
All PFASAR will use the same input
parameters (orbit data, earth
ellipsoid parameters, centre swath
elevation angle, etc.) and the
same software to establish the
output grid. The projection used for
stripline products differs from the
normally used for SAR products.
SAR products are usually projected
such that the first pixel in range is kept at a
constant range. For stripline
products, however, the ground range
projection is designed to allow very
long azimuth extent
images to be displayed without
cutting off any data and with a
minimum of blackfilled pixels. This
is done by keeping the midground
range pixels at a constant
elevation angle.
Radiometric continuity is achieved by
ensuring the Doppler Centroid
frequency is continuous along the
data segment. The input raw data
to the different PFASAR computers
is overlapped and each PFASAR uses
the overlap regions (at the
beginning and end of the slice) to
estimate the Doppler Centroid
frequency. The Doppler Centroid
frequency that is used within the
slice is calculated by interpolation.
The slice size and the overlap depend
on the mode (IM, AP, WS or GM). The
figure and table below show the
minimum and maximum slice sizes
and the minimum overlap. The overlap
must take into account the amount of
data required for Doppler centroid
estimation and processing throw
away (matched filter throwaway and
azimuth skew).

Figure 2.41 Slice Size and Overlap Illustration 
Product generation includes
the following extra parameters for
stripline processing:
 Slice size
 Slice number
 Acquisition start time of the segment
 Overlap size
2.6.1.2.5.1.1 Slice Size and Overlap

Table 2.34 Slice Size and Overlap

Mode 
IM 
AP 
WS 
GM 
MinimumSlice Size 
20.7 sec 
37.4 sec 
5 sec 
30.7 sec 
MaximumSlice Size 
90 sec 
90 sec 
90 sec 
20 min 
MinimumOverlap 
2.5 sec 
2.5 sec 
8.3 sec 
54.3 sec 
2.6.1.2.5.2 Processing Parameters
Affected By Strip Line Product
Continuity Requirements ASAR processing requires
the calculation and use of a number of
processing parameters whose values vary
at different rates. Some parameters
may be held constant throughout an
acquisition segment while other
parameters must be recalculated at
intervals and then applied to blocks
of data or varied smoothly throughout
the segment.
Table 2.35 below
"Processing Parameters
Affecting Stripline
Products", identifies
processing parameters that are
important for stripline processing
and groups the parameters depending
on their source:

signal data 
parameters derived from the SAR
raw data;

auxiliary data
 parameters derived from common
auxiliary data provided to all
ASAR computers;

ISP header data
 parameters extracted from
header of downlink data and may
vary throughout data segments.

Table 2.35 Processing Parameters Affecting Stripline Products

Processing Parameters
from Signal Data 
Processing Parameters
from Auxiliary Data 
Processing Parameters
from ISP Header Data 
I and Q channel statistics 
alongtrack pixel
spacing/start position 
First output range sample 
Chirp Replica 


Antenna Elevation gain correction 
Slant range/ground range
interpolation to output grid 

Doppler Centroid Estimate 


Doppler Ambiguity Estimate 
FM Rate 

2.6.1.2.5.2.1 I And Q Channel Statistics
Channel statistics are calculated
from raw data to determine I and
Q channel bias and orthogonality
adjustments. These adjustments
affect radiometric accuracy. ERS
experience indicates that
channel bias values are
relatively slowly varying,
therefore statistical
analysis of data from any
portion of the data segment
could be applied to the entire
segment, or the analysis could
be repeated using any
portion of the segment and yield
similar results.
Each PFASAR computer evaluates I
and Q channel statistics from
its own ISP data.
The raw data
used for statistics is a
subsample of the total raw data
taken from samples
throughout the input data. The
calculated correction factors
are applied uniformly for the
entire slice.
2.6.1.2.5.2.2 Chirp Replica
The chirp replica
is derived by processing the
periodic calibration
data. Periodic calibration
activities performed by the
instrument generate
calibration data for segments of
the SAR antenna in a cyclic
manner and downlinks this data
interspersed with the imaging
data. The pulse replica is
derived from the summation of
calibration data from all 32
antenna segments received over a
period of time.
During stripline processing the
chirp replica is updated
whenever a new periodic
calibration cycle is found in
the input data. If a
complete set of calibration
pulses from 32 antenna segments
is not found, then the nominal
chirp is used.
2.6.1.2.5.2.3 Antenna Elevation Gain Correction
The antenna elevation gain
correction is also derived by
processing the periodic
calibration data and is also
updated whenever a new
periodic calibration cycle is
found in the input data. If a
complete set of calibration
pulses from 32 antenna segments
is not found, then the
nominal antenna elevation gain
correction is used.
The antenna elevation pattern is
converted to slant range pattern
so it can be applied to the
range compressed data. This
conversion is updated
periodically to follow the
continuously changing
satellite/earth geometry.
2.6.1.2.5.2.4 Doppler Centroid Estimation Doppler centroid
estimation is performed by each
PFASAR computer using the data at
the beginning and end of the slice.
Linear interpolation using the
two estimates is used to compute
Doppler centroid frequency at
various positions along the slice.
If the overlap data is
missing, Doppler centroid
estimation is still done using
the data at the beginning and
end of the available input
data. In this case, the Doppler
centroid may not be continuous
across the adjacent slices. This
is acceptable because there is
no valid image at the
boundary of the slices.
The Doppler centroid frequency
maximum variation is about 1.7
Hz/sec. Over a slice of 20
seconds, the Doppler centroid
frequency changes by a
maximum of 34 Hz and the
variation can be approximated as
a linear function over a long period.
The ambiguity estimate is
performed once per slice and is
then applied throughout that slice.
( For a further discussion of the
Doppler centroid frequency,
ambiguity, and estimation, see
the section entitled "Doppler
Centroid Frequency
Estimator" 2.6.1.2.2. ).
2.6.1.2.5.2.5 alongtrack Pixel
Spacing/Start Position A stripline product is
required to have a constant
alongtrack pixel spacing to meet
geometric requirements and ease
processing of child products
(floating scenes extracted from the
stripline product). For
systematically generated ASAR
products (GMM, IMM, WSM, APM, and
browse), each output line contains
pixels for a specific zero Doppler
time and lines are spaced by a
constant zero Doppler time interval
(rather than constant distance
spacing). Therefore each output line
must be placed on a grid with a
regular time interval referenced to
the start time of the stripline
product segment. The Line
Time Interval in seconds (the
grid spacing alongtrack) is
predefined for each product
type and subswath and is a
constant throughout the
orbit to ensure repeatability of
grid spacing from one orbit to
the next. (It should be noted
that the precise pixel spacing
in metres will vary depending on
the instantaneous velocity of
the satellite.)
Each PFASAR calculates the zero
Doppler start time of the
stripline product segment by
converting the acquisition time
to zero Doppler time.
Each PFASAR then calculates the
zero Doppler start time and stop
time of its output slice.
2.6.1.2.5.2.6 Slant Range/Ground Range
(SR/GR) Conversion To Output Grid The output grid is
defined such that the azimuth line
of the output product is parallel
with the mid swath line, where the
mid swath line is the line which
has a fixed elevation angle along
azimuth. This output grid does not
contain any range pixel with a
constant range time along
azimuth. As the satellite
altitude varies with time, the
mid swath line moves closer or
farther away from the
subsatellite track and the valid
data follows the Sampling Window
Start Time (SWST).
The number of samples per range
lines in the output product is
set to be large enough to cover
the shift of the valid data
along the segment so that no
valid data is thrown away.
The valid samples are projected
to the output grid using an
SR/GR conversion table. PFASAR
calculates the SR/GR conversion
table periodically along the
azimuth direction and performs
linear interpolation between
adjacent SR/GR conversion tables
so that a smoothly varying
SR/GR can be achieved.
2.6.1.2.5.2.7 FM Rate The Frequency
Modulation (FM) Rate must be updated
throughout the segment to maintain
geometric continuity. The FM rate
varies slowly so that
calculations at consecutive portions
of the segment do not result in
discontinuity for mediumresolution
products. It is calculated once
per slice.
2.6.1.2.5.2.8 First Output Range Sample The location of the
first valid output range sample is a
function of the Sampling Window
Start Time (SWST). PFASAR
calculates the slant range of
this first valid sample using the
SWST and then projects it to the
output grid using the smoothly
varying SR/GR conversion table.
2.6.1.3 Level 1B Accuracies
The following two tables define the product
geometric accuracies of the Level1B products.

Table 2.36 ASAR High Resolution Image Accuracy

Product Type

IM (Image Mode) 
AP (Alternating Polarisation Mode) 
WV (Wave Mode) 
Precision Image

Pixel=12.5m
Resolution < 28m
ENL = 3.9

Pixel=12.5m
Resolution < 30m
ENL = 1.9


SingleLook Complex

Pixel=image spacing
Resolution=6m (Azimuth) and
9m(Slant Range)

Pixel=image spacing
Resolution=12m (Azimuth) and
9m(Slant Range)


Ellipsoid Geocoded

Pixel=12.5m
Resolution < 30m
ENL = 3.9

Pixel=12.5m
Resolution < 30m
ENL > 1.9


SingleLook Complex Wave
Imagette



Pixel=image spacing
Resolution=6m (Azimuth) and
9m(Slant Range)


Table 2.37 ASAR Brows, Medium Resolution and Global Monitoring Image Accuracy

Product Type

IM (Image Narrow swath) 
AP (Alternating Polarisation) 
WS (Wide Swath) 
GM (Global Monitoring) 
Medium Resolution Image

Resolution < 150m
Pixel=75m
ENL = 40

Resolution < 150m
Pixel=75m
ENL = 50

Resolution < 150m
Pixel=75m
ENL = 11*


Global Monitoring Image




Resolution < 950m
Pixel=500m
ENL = 7 to 9*

Browse Image **

Pixel=225m
ENL = 80

Pixel=225m
ENL = 75

Pixel=900m
ENL = 57to 82

Resolution =2000m
Pixel=1000m
ENL = 18 to 21

In the tables, Pixel means pixel spacing,
Resolution means spatial resolution, ENL is
equvalent number of looks (which affects
radiometric resolution). The ENL on some
images is dependant on the instrument
setting, indicated with one asterisk (*).
Browse images are derived from Medium
Resolution images.

Table 2.38 Summary of ASAR Predicted Performance with Comparison to ERS

Parameter 
Unit 
Image 
Alternating Polarisation 
Wide Swath 
Global Monitoring 
Wave 
ERS1/2 
Polarisation 

VV or HH 
VV/HH, HH/HV, VV/VH 
VV or HH 
VV or HH 
VV or HH 
VV 
Spatial resolution (a x r) 
m 
27.5 x 28.1 
28.7 x 29.7 
149 x 145 
949 x 977 
27.5 x 29.6 
27.5 x 28.1 
Radiometric resolution 
dB 
1.54 
2.46 to 2.50 
1.45 to 1.72 
1.35 to 1.44 
1.54 
2.07 
PTAR azimuth 
dB 
25.9 to 29.6 
19.1 to 28.0 
22.3 to 28.6 
26.6 to 29.3 
27.3 to 29.6 
27.8/24.5 
PTAR range 
dB 
31.6 to 45.8 
26.4 to 40.5 
25.0 to 33.9 
25.0 to 32.2 
31.2 to 45.7 
>40 
DTAR azimuth 
dB 
22.6 to 24.7 
18.1 to 24.5 
20.3 to 24.9 
24.6 to 27.5 
22.6 to 24.7 
>25 
DTAR range 
dB 
17.1 to 39.4 
17.1 to 39.4 
17.1 to 30.8 
17.1 to 30.8 
21.2 to 47.7 
>35 
Radiometric stability (1sigma) 
dB 
0.32 to 0.40 
0.50 to 0.55 
0.32 to 0.42 
0.46 to 0.53 
0.55 to 0.60 
0.24/0.27 
Radiometric accuracy (3sigma) 
dB 
1.17 to 1.38 
1.62 to 1.81 
1.20 to 1.45 
1.54 to 1.74 
1.80 to 1.94 
na 
Noise Equivalent (sigmanought) 
dB 
19.6 to 22.1 
19.4 to 21.9 
20.8 to 26.2 
31.5 to 35 
19.8 to 22.4 
26.2/25.2 
