Agilent Spectrum Analysis Basics - Agilent Technologies

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Another specification is listed for the resolution filters: bandwidth selectivity (or selectivity or shape factor). Bandwidth selectivity helps determine the resolving power for unequal sinusoids. For Agilent analyzers, bandwidth selectivity is generally specified as the ratio of the 60 dB bandwidth to the 3 dB bandwidth, as shown in Figure 2-9. The analog filters in Agilent analyzers are a four-pole, synchronously-tuned design, with a nearly Gaussian shape 4 . This type of filter exhibits a bandwidth selectivity of about 12.7:1. 3 dB 60 dB Figure 2-9. Bandwidth selectivity, ratio of 60 dB to 3 dB bandwidths For example, what resolution bandwidth must we choose to resolve signals that differ by 4 kHz and 30 dB, assuming 12.7:1 bandwidth selectivity? Since we are concerned with rejection of the larger signal when the analyzer is tuned to the smaller signal, we need to consider not the full bandwidth, but the frequency difference from the filter center frequency to the skirt. To determine how far down the filter skirt is at a given offset, we use the following equation: Where H(∆f) = –10(N) log 10 [(∆f/f 0 ) 2 + 1] H(∆f) is the filter skirt rejection in dB N is the number of filter poles ∆f is the frequency offset from the center in Hz, and RBW f 0 is given by 2 √2 1/N –1 For our example, N=4 and ∆f = 4000. Let’s begin by trying the 3 kHz RBW filter. First, we compute f 0 : 3000 f 0 = = 3448.44 2 √2 1/4 –1 Now we can determine the filter rejection at a 4 kHz offset: 4. Some older spectrum analyzer models used five-pole filters for the narrowest resolution bandwidths to provide improved selectivity of about 10:1. Modern designs achieve even better bandwidth selectivity using digital IF filters. 18 H(4000) = –10(4) log 10 [(4000/3448.44) 2 + 1] = –14.8 dB This is not enough to allow us to see the smaller signal. Let’s determine H(∆f) again using a 1 kHz filter: 1000 f 0 = = 1149.48 2 √2 1/4 –1
This allows us to calculate the filter rejection: H(4000) = –10(4) log 10 [(4000/1149.48) 2 + 1] = –44.7 dB Thus, the 1 kHz resolution bandwidth filter does resolve the smaller signal. This is illustrated in Figure 2-10. Figure 2-10. The 3 kHz filter (top trace) does not resolve smaller signal; reducing the resolution bandwidth to 1 kHz (bottom trace) does Digital filters Some spectrum analyzers use digital techniques to realize their resolution bandwidth filters. Digital filters can provide important benefits, such as dramatically improved bandwidth selectivity. The Agilent PSA Series spectrum analyzers implement all resolution bandwidths digitally. Other analyzers, such as the Agilent ESA-E Series, take a hybrid approach, using analog filters for the wider bandwidths and digital filters for bandwidths of 300 Hz and below. Refer to Chapter 3 for more information on digital filters. Residual FM Filter bandwidth is not the only factor that affects the resolution of a spectrum analyzer. The stability of the LOs in the analyzer, particularly the first LO, also affects resolution. The first LO is typically a YIG-tuned oscillator (tuning somewhere in the 3 to 7 GHz range). In early spectrum analyzer designs, these oscillators had residual FM of 1 kHz or more. This instability was transferred to any mixing products resulting from the LO and incoming signals, and it was not possible to determine whether the input signal or the LO was the source of this instability. The minimum resolution bandwidth is determined, at least in part, by the stability of the first LO. Analyzers where no steps are taken to improve upon the inherent residual FM of the YIG oscillators typically have a minimum bandwidth of 1 kHz. However, modern analyzers have dramatically improved residual FM. For example, Agilent PSA Series analyzers have residual FM of 1 to 4 Hz and ESA Series analyzers have 2 to 8 Hz residual FM. This allows bandwidths as low as 1 Hz. So any instability we see on a spectrum analyzer today is due to the incoming signal. 19
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: Agilent data sheets describe the ab
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 and 34: EMI detectors: average and quasi-pe
Page 35 and 36: The effect is most noticeable in me
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
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2D dB 3D dB 3D dB 3D dB With a cons
Page 73 and 74:
We can construct a similar line for
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Figure 6-2 shows the dynamic range
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Dynamic range versus measurement un
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Let’s see what happened to our dy
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The range of the log amplifier can
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Chapter 7 Extending the Frequency R
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Next let’s see to what extent har
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In examining Figure 7-5, we find so
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Can we conclude from this discussio
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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
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Table 7-1 shows the harmonic mixing
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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
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Data transfer and remote instrument
Page 109 and 110:
Summary The objective of this appli
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Delta marker: A mode in which a fix
Page 113 and 114:
Frequency stability: A general phra
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LO feedthrough: The response on the
Page 117 and 118:
Residual responses: Discrete respon
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Video: In a spectrum analyzer, a te
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Agilent Spectrum Analysis Basics - Agilent Technologies

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