I’m not talking about the quality, just the variance
The basic amplitude accuracy of today’s signal analyzers is amazingly good, sometimes significantly better than ±0.2 dB. Combining this accuracy with precise frequency selectivity over a range of bandwidths—from very narrow to very wide—yields good power measurements of simple or complex signals. It’s great for all of us who seek better measurements!
However, if you’re working with time-varying or noisy signals—including almost all measurements made near noise—you’ve probably needed to do some averaging to reduce measurement variance as a way to improve amplitude accuracy.
As a matter of fact, you may already be doing two or more types of averaging at once. Here’s a summary of the four main averaging processes in spectrum/signal analyzers:
- Video bandwidth filtering is the traditional averaging technique of swept spectrum analyzers. The signal representing the detected magnitude and driving the display’s Y-axis is lowpass filtered.
- Trace averaging is a newer technique in which the value of each trace point (bin or bucket) is averaged each time a new sweep is made.
- The average detector is a type of display detector that combines all the individual measurements making up each trace point into an average for that point.
- Band power averaging combines a specified range of trace points to calculate a single value for a frequency band.
Depending on how you set up a measurement, some or all of these averaging processes may be operating together to produce the results you see.
The use of multiple averaging processes may be desirable and effective, but as I mentioned in The Average of the Log is Not the Log of the Average, different types of averages—different averaging scales—can produce different average values for the same signal.
How do you make the best choice for your measurement, and make sure the averaging scales used are consistent? The good news is that in most cases there is nothing you need to do. Agilent signal analyzers will ensure that consistent averaging scales are used, locking the scale to one of three: power, voltage or log-power (dB).
In addition, Agilent analyzers choose the appropriate scale depending on the type of measurement you’re making. Selecting marker types and measurement applications—such as adjacent channel power, spurious or phase noise—gives the analyzer all the information it needs to make an accurate choice.
If you’re making a more general measurement in which the analyzer does not know the characteristics of the signal, there are a couple of choices you can make to ensure accurate results and optimize speed.
When you want to quickly reduce variance and get accurate results—regardless of signal characteristics—use the average detector.
Beyond the accuracy it provides for all signal types, the average detector is extremely efficient at quickly reducing variance and is very easy to optimize: If you want more averaging, just select a slower sweep speed. The analyzer will have more time to make individual measurements for each display point and will automatically add them to the average. Simply keep on reducing sweep speed until you get the amount of averaging you want.
The exception to this approach is when you’re measuring small CW spurs near noise, and in that case you may want to use a narrower video bandwidth filter for averaging.
With these two approaches you’ll improve both the quality of your signal measurements and the variance, with a minimum of effort and no accidental inconsistencies. Once again, a combination of techniques provides the desired results. For more detail, see Chapter 2 of the updated Application Note 150 Spectrum Analysis Basics.