Agilent Spectrum Analysis Basics - Agilent Technologies

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Video filtering Discerning signals close to the noise is not just a problem when performing EMC tests. Spectrum analyzers display signals plus their own internal noise, as shown in Figure 2-27. To reduce the effect of noise on the displayed signal amplitude, we often smooth or average the display, as shown in Figure 2-28. Spectrum analyzers include a variable video filter for this purpose. The video filter is a low-pass filter that comes after the envelope detector and determines the bandwidth of the video signal that will later be digitized to yield amplitude data. The cutoff frequency of the video filter can be reduced to the point where it becomes smaller than the bandwidth of the selected resolution bandwidth (IF) filter. When this occurs, the video system can no longer follow the more rapid variations of the envelope of the signal(s) passing through the IF chain. The result is an averaging or smoothing of the displayed signal. Figure 2-27. Spectrum analyzers display signal plus noise Figure 2-28. Display of figure 2-27 after full smoothing 34
The effect is most noticeable in measuring noise, particularly when a wide resolution bandwidth is used. As we reduce the video bandwidth, the peakto-peak variations of the noise are reduced. As Figure 2-29 shows, the degree of reduction (degree of averaging or smoothing) is a function of the ratio of the video to resolution bandwidths. At ratios of 0.01 or less, the smoothing is very good. At higher ratios, the smoothing is not so good. The video filter does not affect any part of the trace that is already smooth (for example, a sinusoid displayed well out of the noise). Figure 2-29. Smoothing effect of VBW-to-RBW ratios of 3:1, 1:10, and 1:100 35
Page 1 and 2: Agilent Spectrum Analysis Basics Ap
Page 3 and 4: Table of Contents — continued Cha
Page 5 and 6: Some measurements require that we p
Page 7 and 8: Figure 1-3. Harmonic distortion tes
Page 9 and 10: While we have defined spectrum anal
Page 11 and 12: Since the output of a spectrum anal
Page 13 and 14: We need to pick an LO frequency and
Page 15 and 16: To separate closely spaced signals
Page 17 and 18: Agilent data sheets describe the ab
Page 19 and 20: This allows us to calculate the fil
Page 21 and 22: Some modern spectrum analyzers allo
Page 23 and 24: On the other hand, the rise time of
Page 25 and 26: The width of the resolution (IF) fi
Page 27 and 28: The “bucket” concept is importa
Page 29 and 30: Peak (positive) detection One way t
Page 31 and 32: The normal detection algorithm: If
Page 33: EMI detectors: average and quasi-pe
Page 37 and 38: Thus, the display gradually converg
Page 39 and 40: In some cases, time-gating capabili
Page 41 and 42: Timeslots 0 1 2 3 4 5 6 7 Timeslots
Page 43 and 44: Gated sweep Gated sweep, sometimes
Page 45 and 46: In Chapter 2, we did a filter skirt
Page 47 and 48: Custom signal processing IC Turning
Page 49 and 50: Chapter 4 Amplitude and Frequency A
Page 51 and 52: Following the input filter are the
Page 53 and 54: Improving overall uncertainty When
Page 55 and 56: Examples Let’s look at some ampli
Page 57 and 58: In a factory setting, there is ofte
Page 59 and 60: Because the input attenuator has no
Page 61 and 62: Noise figure Many receiver manufact
Page 63 and 64: Using these expressions, we’ll se
Page 65 and 66: Let’s first test the two previous
Page 67 and 68: This is the 2.5 dB factor that we a
Page 69 and 70: When we add a preamplifier to our a
Page 71 and 72: 2D dB 3D dB 3D dB 3D dB With a cons
Page 73 and 74: We can construct a similar line for
Page 75 and 76: Figure 6-2 shows the dynamic range
Page 77 and 78: Dynamic range versus measurement un
Page 79 and 80: Let’s see what happened to our dy
Page 81 and 82: The range of the log amplifier can
Page 83 and 84: Chapter 7 Extending the Frequency R
Page 85 and 86:
Next let’s see to what extent har
Page 87 and 88:
In examining Figure 7-5, we find so
Page 89 and 90:
Can we conclude from this discussio
Page 91 and 92:
Amplitude calibration So far, we ha
Page 93 and 94:
From the graph, we see that a -10 d
Page 95 and 96:
Pluses and minuses of preselection
Page 97 and 98:
Table 7-1 shows the harmonic mixing
Page 99 and 100:
Figure 7-15. Which ones are the rea
Page 101 and 102:
Figure 7-18. The image suppress fun
Page 103 and 104:
Other examples of built-in measurem
Page 105 and 106:
Digital modulation analysis The com
Page 107 and 108:
Data transfer and remote instrument
Page 109 and 110:
Summary The objective of this appli
Page 111 and 112:
Delta marker: A mode in which a fix
Page 113 and 114:
Frequency stability: A general phra
Page 115 and 116:
LO feedthrough: The response on the
Page 117 and 118:
Residual responses: Discrete respon
Page 119 and 120:
Video: In a spectrum analyzer, a te
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