Getting Started with Your 2700 Series Instrument

Chapter 5: Quick Guides The FFT Spectrum Analyzer
Real-Time vs. Batch-Mode Instruments
The Analog Analyzer is a real-time instrument. That is, it returns readings
continuously, whether or not a sweep is in progress. These readings include
level and frequency, as well as functions such as distortion or phase. During a
sweep, readings from the audio analyzer are diverted from the panel meters to
the sweep engine for graphing. After the sweep, the readings are once again
displayed in the panel meters.
By contrast, the FFT Spectrum Analyzer is a batch-mode instrument. It does
not generate readings if a sweep is not being performed. When a sweep is
started, the Spectrum Analyzer first acquires a segment of audio by storing it
in memory. This is indicated (briefly) on the Status bar as DSP Acquiring
Data. Once the acquisition is complete, the Spectrum Analyzer processes the
data. This is indicated as DSP Transforming Data (again, briefly) on the Status
bar. Once processing is complete, the control software fetches a batch of
readings from the Spectrum Analyzer. At that point, the data is graphed.
The FFT Concept
The FFT (fast Fourier transform) is an efficient way to decompose a periodic
signal into its component frequencies. It transforms a time-domain signal
(such as you might see in an oscilloscope trace) into a frequency-domain representation.
As an example, a high-quality power amplifier fed with a sinusoidal input
will produce harmonic distortion at its output that is invisible in an oscilloscope
trace. If an FFT is performed on the output signal, however, the individual
distortion harmonics can be seen. The pattern of harmonics provides
insight into the mechanisms responsible for the distortion.
The FFT process consists of the following steps:
Triggering. The system waits for a trigger event from the selected
source, for instance, a positive-going zero crossing of the analog generator.
Acquisition. The system stores the input audio in memory until the selected
number of audio samples has been received. The more audio data
acquired, the more frequency points there will be in the result, and hence
the higher the resolution will be. However, longer acquisitions take more
time.
Windowing. Before transformation, the audio data is shaped in time by
multiplying it with a pre-defined envelope. This increases the accuracy
of the amplitude values returned from the FFT.
46 Getting Started with Your 2700 Series Instrument