Originally posted Jan 30, 2014
Better spectrum measurements through data reduction
Like most engineers using spectrum and signal analyzers, I make measurements without paying much attention to the “detector” settings. These are visible in the menus and in a little grid near the top of the screen that summarizes which detectors are in use on each trace. Occasionally, I notice that changes to parameters such as RBW and VBW affect the look of the trace. The same is true for choices such as selecting a marker to read noise level, harmonics or band power.
If I’m aware of detectors at all, I trust the analyzer to make appropriate choices—and that’s a good thing. Most modern analyzers make reasonable and safe default detector choices—at least in terms of measurement accuracy—especially when users tell them what sort of measurement is desired by selecting a measurement or marker type.
Of course, the analyzer is not clairvoyant and therefore the default setting and linkages aren’t always optimal. If you want to optimize for better measurements, it helps to know more about what the detector is and does. In this post I’ll explain the function of the detectors and take the first step toward helping you choose the one that’s optimal for your measurements.
But first, I’ll describe which detector it isn’t, as shown in the figure below. The diagram describes a spectrum analyzer from the IF onward in conceptual terms, and many modern analyzers perform these operations with DSP.
This partial, simplified block diagram of a spectrum analyzer shows the two detectors and their locations in the signal path. The IF or video detector converts the IF signal into a magnitude value and has no user controls. The display-detector or detector-mode setting is controlled by the user and determines how changing magnitude values are converted to measurement points.
The IF or video or envelope detector converts the IF output to a DC “video” signal proportional to the magnitude of the IF output. It functions as an AM demodulator and, as the symbol indicates, it was sometimes implemented as a diode circuit. There’s a rich history here, including cat’s-whisker detectors and coherers going back more than a century! Unfortunately, that technology and wild things like spark-gap radios will have to wait for a future post.
For the analyzer user, the real action happens downstream at the display detector. This circuit or algorithm performs one or more types of data reduction, as shown in the figure below.
The function of a display detector is enlarged and shown over an interval of slightly more than one display point or bucket. The display detector reduces many measurements of IF magnitude to a single one for display, here selecting a maximum or minimum or an unbiased sample.
The decisions inherent in the data reduction can be very helpful in focusing the measurement on the intended target. For example, a peak detector can ensure that spurious signals falling between display points are never missed. A sample detector can avoid bias and improve accuracy in measuring noise. An average detector—the subject of a future post—is a powerful tool for reducing variance caused by noise and modulation.
As you can see from these examples, the optimum detector will depend on the measurement you’re trying to make. Agilent signal analyzers choose different detectors automatically, matching them to the setup choices you make and any measurement applications you may be using. When you’re interested in multiple measurements of the same signal, Agilent analyzers allow different detectors to be used simultaneously for each trace in a single sweep.
Detectors are both important and useful, affecting measurement speed, accuracy and dynamic range. I’ll have more examples in posts to come, and in the meantime you can learn more in the new, updated version of application note 150 Spectrum Analysis Basics.