LeCroy SDA, Руководство оператора

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SDA-OM-E Rev H
An FFT operation on an N-point time domain signal can be compared to passing the signal
through a comb filter consisting of a bank of N/2 filters. All the filters have the same shape and
width and are centered at N/2 discrete frequencies. Each filter collects the signal energy that falls
into the immediate neighborhood of its center frequency. Thus it can be said that there are N/2
frequency bins. The distance in Hz between the center frequencies of two neighboring bins is
always the same: Delta f.
Power (Density) Spectrum
Because of the linear scale used to show magnitudes, lower amplitude components are often
hidden by larger components. In addition to the functions offering magnitude and phase
representations, the FFT option offers power density and power spectrum density functions.
These latter functions are even better suited for characterizing spectra. The power spectrum (V
2
)
is the square of the magnitude spectrum (0 dBm corresponds to voltage equivalent to 1 mW into
50
Ω ). This is the representation of choice for signals containing isolated peaks — periodic
signals, for instance.
The power density spectrum (V
2
/Hz) is the power spectrum divided by the equivalent noise
bandwidth of the filter associated with the FFT calculation. This is best employed for
characterizing broadband signals such as noise.
Memory for FFT
The amount of acquisition memory available will determine the maximum range (Nyquist
frequency) over which signal components can be observed. Consider the problem of determining
the length of the observation window and the size of the acquisition buffer if a Nyquist rate of 500
MHz and a resolution of 10 kHz are required. To obtain a resolution of 10 kHz, the acquisition
time must be at least:
T = 1/Delta f = 1/10 kHz = 100 ms
For a digital oscilloscope with a memory of 100 kB, the highest frequency that can be analyzed is:
Delta f x N/2 = 10 kHz x 100 kB/2 = 500 MHz
FFT Pitfalls to Avoid
Take care to ensure that signals are correctly acquired: improper waveform positioning within the
observation window produces a distorted spectrum. The most common distortions can be traced
to insufficient sampling, edge discontinuities, windowing or the "picket fence" effect.
Because the FFT acts like a bank of band-pass filters centered at multiples of the frequency
resolution, components that are not exact multiples of that frequency will fall within two
consecutive filters. This results in an attenuation of the true amplitude of these components.
Picket Fence and Scallop
The highest point in the spectrum can be 3.92 dB lower when the source frequency is halfway
between two discrete frequencies. This variation in spectrum magnitude is the picket fence effect.
The corresponding attenuation loss is referred to as scallop loss. LeCroy scopes automatically
correct for the scallop effect, ensuring that the magnitude of the spectra lines correspond to their
true values in the time domain.