LeCroy SDA, Operator'S Manual

Page: 183 / 397

SDA Operator’s Manual
SDA-OM-E Rev H
183
Summed Averaging
Summed Averaging is the repeated addition, with equal weight, of successive source waveform
records. If a stable trigger is available, the resulting average has a random noise component
lower than that of a single-shot record. Whenever the maximum number of sweeps is reached,
the averaging process stops.
An even larger number of records can be accumulated simply by changing the number in the
dialog. However, the other parameters must be left unchanged or a new averaging calculation will
be started. You can pause the averaging by changing the trigger mode from NORM/AUTO to
STOP. The instrument resumes averaging when you change the trigger mode back to
NORM/AUTO.
You can reset the accumulated average by pushing the CLEAR SWEEPS button or by changing
an acquisition parameter such as input gain, offset, coupling, trigger condition, timebase, or
bandwidth limit. The number of current averaged waveforms of the function, or its zoom, is shown
in the acquisition status dialog. When summed averaging is performed, the display is updated at
a reduced rate to increase the averaging speed (points and events per second).
Continuous Averaging
Continuous Averaging, the default setting, is the repeated addition, with unequal weight, of
successive source waveforms. It is particularly useful for reducing noise on signals that drift very
slowly in time or amplitude. The most recently acquired waveform has more weight than all the
previously acquired ones: the continuous average is dominated by the statistical fluctuations of
the most recently acquired waveform. The weight of ‘old’ waveforms in the continuous average
gradually tends to zero (following an exponential rule) at a rate that decreases as the weight
increases.
The formula for continuous averaging is
new average = (new data + weight * old average)/( 1)
This is also the formula used to compute summed averaging. But by setting a "sweeps" value,
you establish a fixed weight that is assigned to the old average once the number of "sweeps" is
reached. For example, for a sweeps (weight) value of
4 :
1
st
sweep (no old average yet): new average = (new data +0 * old average)/(0 + 1) = new data
only
2
nd
sweep: new average = (new data + 1*old average)/(1 + 1) = 1/2 new data +1/2 old average
3
rd
sweep: new average = (new data + 2 * old average)/(2 + 1) = 1/3 new data + 2/3 old average
4
th
sweep: new average = (new data + 3 * old average)/(3 + 1) = 1/4 new data + 3/4 old average
5
th
sweep: new average = (new data + 4 * old average)/(4 + 1) = 1/5 new data + 4/5 old average
6
th
sweep: new average = (new data + 4 * old average)/(4 + 1) = 1/5 new data + 4/5 old average
7
th
sweep: new average = (new data + 4 * old average)/(4 + 1) = 1/5 new data + 4/5 old average
In this way, for sweeps > 4 the importance of the old average begins to decrease exponentially.