How to Use Spectrum Analyzer for Noise Testing? Part 1

- May 16, 2018 -

The spectrum analyzer is a powerful tool for making noise measurements. In general, spectrum analyzers can display the relation between power (or voltage) and frequency, which is similar to the noise spectral density curve. In fact, some spectrum analyzers have a special mode of operation that allows the measurement results to be displayed directly in spectral density units (ie nV/rt-Hz). In other cases, the measurement result must be multiplied by a correction factor to convert the relevant unit of measure into spectral density units.

spectrum analyzers, like oscilloscopes, are both digital and analog. One method of generating a spectral curve by an analog spectrum analyzer is to sweep a band-pass filter at various frequencies while plotting the measured output value of the filter. Another approach is to use superheterodyne reception, which performs scanning of the local oscillator at various frequencies. However, digital spectrum analyzers use fast Fourier transforms to generate the spectrum (often used with superheterodyne receiving technology).

Although the spectrum analyzers used are of different models, some major parameters still need to be considered. The start and stop frequencies indicate the frequency range over which the bandpass filter is scanned. The resolution bandwidth is the width of the bandpass filter scanned in the frequency range. Reducing the resolution bandwidth will increase the ability of the spectrum analyzer to process signals at discrete frequencies while extending the scan time. Figure 1 shows the operation of the scan filter. Figure 2 and Figure 3 show the results obtained when the same spectrum analyzer uses different resolution bandwidths. In Figure 2, because the resolution bandwidth is set very small, discrete frequency components (ie, 150 Hz) are properly handled. On the other hand, in Fig. 3, the discrete frequency component (ie, 1200 Hz) has not been properly handled because the resolution bandwidth is set to be very large.


Figure 1.


Figure 2.


Figure 3.

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