ANEKA INFO TEKNIK: SPECTRUM ANALYZER; Peak (positive) detection

Peak (positive) detection
One way to insure that all sinusoids are reported at their true amplitudes is to display the maximum value encountered in each bucket. This is the positive peak detection mode, or peak. This is illustrated in Figure 2-22b. Peak is the default mode offered on many spectrum analyzers because it ensures that no sinusoid is missed, regardless of the ratio between resolution bandwidth and bucket width. However, unlike sample mode, peak does not give a good representation of random noise because it only displays the maximum value in each bucket and ignores the true randomness of the noise. So spectrum analyzers that use peak detection as their primary mode generally also offer the sample mode as an alternative.

Negative peak detection
Negative peak detection displays the minimum value encountered in each bucket. It is generally available in most spectrum analyzers, though it is not used as often as other types of detection. Differentiating CW from impulsive signals in EMC testing is one application where negative peak detection is valuable. Later in this application note, we will see how negative peak detection is also used in signal identification routines when using external
mixers for high frequency measurements.

Figure 2-23a. Normal mode
Figure 2-23b. Sample mode
Figure 2-23. Comparison of normal and sample display detection when measuring noise

Normal detection
To provide a better visual display of random noise than peak and yet avoid the missed-signal problem of the sample mode, the normal detection mode (informally known as rosenfell 9 ) is offered on many spectrum analyzers. Should the signal both rise and fall, as determined by the positive peak and negative peak detectors, then the algorithm classifies the signal as noise. In that case, an odd-numbered data point displays the maximum value encountered during its bucket. And an even-numbered data point displays the minimum value encountered during its bucket. See Figure 2-25. Normal and sample modes are compared in Figures 2-23a and 2-23b
9. rosenfell is not a person's name but rather a description of the algorithm that tests to see if the signal rose and fell within the bucket represented by a given data point. It is also sometimes written as "rose'n' fell".
10. Because of its usefulness in measuring noise, the sample detector is usually used in noise marker applications. Similarly, the measurement of channel power and adjacent-channel power requires a detector type that gives results unbiased by peak detection. For analyzers without averaging detectors, sample detection is the best choice.
What happens when a sinusoidal signal is encountered? We know that as a mixing product is swept past the IF filter, an analyzer traces out the shape of the filter on the display. If the filter shape is spread over many display points, then we encounter a situation in which the displayed signal only rises as the mixing product approaches the center frequency of the filter and only falls as the mixing product moves away from the filter center frequency. In either of
these cases, the pos-peak and neg-peak detectors sense an amplitude change in only one direction, and, according to the normal detection algorithm, the maximum value in each bucket is displayed. See Figure 2-24.

What happens when the resolution bandwidth is narrow, relative to a bucket? The signal will both rise and fall during the bucket. If the bucket happens to be an odd-numbered one, all is well. The maximum value encountered in the bucket is simply plotted as the next data point. However, if the bucket is even-numbered, then the minimum value in the bucket is plotted. Depending on the ratio of resolution bandwidth to bucket width, the minimum value can
differ from the true peak value (the one we want displayed) by a little or a lot. In the extreme, when the bucket is much wider than the resolution bandwidth, the difference between the maximum and minimum values encountered in the bucket is the full difference between the peak signal value and the noise. This is true for the example in Figure 2-25. See bucket 6. The peak value of the previous bucket is always compared to that of the current bucket. The
greater of the two values is displayed if the bucket number is odd as depicted in bucket 7. The signal peak actually occurs in bucket 6 but is not displayed until bucket 7.

Figure 2-24. Normal detection displays maximum values in buckets where signal only rises or only falls

The normal detection algorithm:

If the signal rises and falls within a bucket: Even numbered buckets display the minimum (negative peak) value in the bucket. The maximum is remembered. Odd numbered buckets display the maximum (positive peak) value determined by comparing the current bucket peak with the previous (remembered) bucket peak. If the signal only rises or only falls within a bucket, the peak is displayed. See Figure 2-25.

This process may cause a maximum value to be displayed one data point too far to the right, but the offset is usually only a small percentage of the span. Some spectrum analyzers, such as the Agilent PSA Series, compensate for this potential effect by moving the LO start and stop frequencies.

Another type of error is where two peaks are displayed when only one actually exists. Figure 2-26 shows what might happen in such a case. The outline of the two peaks is displayed using peak detection with a wider RBW.

So peak detection is best for locating CW signals well out of the noise. Sample is best for looking at noise, and normal is best for viewing signals and noise.