Swept-tuned spectrum analyzer. Gianfranco Miele, Ph.D
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1 Swept-tuned spectrum analyzer Gianfranco Miele, Ph.D
2 Reference level and logarithmic amplifier The signal displayed on the instrument screen is the envelope of the filtered and amplified IF signal. IF signal power Logarithmic amplifier dynamic range
3 Reference level and logarithmic amplifier The signal displayed on the instrument screen is the envelope of the filtered and amplified IF signal. IF signal power
4 IF amplifier role power Reference level Reference level Input signal power equal to the reference level
5 IF amplifier role power Reference level Reference level RF Att. mixer level Input signal power is attenuated in order not to overload the mixer
6 IF amplifier role power Reference level Reference level RF Att. mixer level IF gain Logarithmic amplifier dynamic range IF amplifier amplifies the IF signal in order to reach the exact vertical position on the screen
7 IF amplifier role power Reference level Reference level RF Att. IF gain mixer level Logarithmic amplifier dynamic range Input signal power level lower than reference.
8 power What happens if the RF attenuation Reference level increases Reference level RF Att. IF gain mixer level Logarithmic amplifier dynamic range Does this operation lead to a correct measure?
9 power What happens if the RF attenuation Reference level increases Reference level RF Att. mixer level IF gain Logarithmic amplifier dynamic range A change in input attenuation will automatically change the IF gain to offset the effect of the change in input attenuation, thereby keeping the signal at a constant position on the display.
10 Block diagram RF input section IF section x(t) RF Att. LPF f x f IF IF amplifier IF filter Logarithmic amplifier f LO Reference Oscillator Local Oscillator Saw-tooth wave generator Display Video section Video filter Envelope detector
11 RF attenuation vs noise level In order to reduce the probability to overload the mixer a possible idea could be to increase input attenuation. Because the spectrum analyzer s noise is generated after the input attenuator, the attenuator setting affects the signalto-noise ratio (SNR). If gain is coupled to the input attenuator to compensate for any attenuation changes, real signals remain stationary on the display. However, displayed noise level changes with IF gain, reflecting the change in SNR that result from any change in input attenuator setting.
12 RF attenuation vs noise level An amplifier at the mixer s output then amplifies the attenuated signal to keep the signal peak at the same point on the analyzer s display. In addition to amplifying the input signal, the noise present in the analyzer is amplified as well, raising the displayed noise level of the spectrum analyzer.
13 IF filter The IF filter is used to identify which portion of the IFconverted input signal has to be displayed at a certain point on the frequency axis. Supposing to analyze purely sinusoidal signal, one would expect a single spectral line but the signal displayed has a bandwidth not equal to 0 Hz. From AN 150 Agilent Spectrum Analysis Basics - Copyright Agilent Technologies
14 IF filter The displayed trace represents the characteristic shape (transfer function) of the bandpass filter. From AN 150 Agilent Spectrum Analysis Basics - Copyright Agilent Technologies
15 RBW The spectral resolution of the analyzer is mainly determined by the resolution bandwidth (RBW), that is, the bandwidth of the IF filter (3 db bandwidth). RBW is defined as the minimum frequency offset required between two signals of equal level to make the signals distinguishable by a dip of about 3 db between the two peaks.
16 IF filter As a consequence a good IF filter in order to correctly analyze very close frequency components must have a narrow band. But this is not the only important feature that a IF filter must have. In fact, let us to consider two neighboring signals having a frequency offset equal to the RBW and distinctly different levels.
17 Selectivity From C. Rauscher "Fundamentals of Spectrum Analysis" Rohde&Schwarz
18 Selectivity The skirt selectivity of the IF filter is also important and is referred to as the selectivity of a filter. The skirt selectivity is specified in form of the shape factor which is calculated as follows: where B 60 db is 60 db bandwidth B 3 db is 3 db bandwidth SF = B 60 db B 3 db
19 Selectivity From C. Rauscher "Fundamentals of Spectrum Analysis" Rohde&Schwarz
20 Ideal IF filter Ideal IF filter are rectangular filters. In fact their SF is equal to 1. But their transient response is unsuitable for spectrum analysis. Since such a filter has a long transient time, the input signal spectrum could be converted to the IF only by varying the LO frequency very slowly to avoid level errors. H f f IF f
21 Actual IF filter Gaussian filters optimized for transients and have SF=4-5. Actual spectrum analyzers have four-pole filters with a nearly Gaussian shape, characterized by a SF=10-14, with a minimum bandwidth equal to 1 khz. H f f IF f
22 RBW and sweep time Because of RBW denotes the minimum offset between two close frequency components having the same amplitude to be distinctly measured, we might desire to have an instrument with the narrowest RBW. Unfortunately RBW influences the measurement time and in particular the sweep time. In fact a filter with a non-zero bandwidth has a settling time with k 1. τ = k RBW
23 RBW and sweep time In order to have a reliable response the signal must be in the filter bandwidth for a time Being the sweep speed t τ = The time that the signal is in the filter bandwidth is k RBW v = span t sweep t = RBW v = RBW span t sweep
24 RBW and sweep time As a consequence, This means that a change in resolution has a dramatic effect on sweep time. Spectrum analyzers automatically couple sweep time to the span and resolution bandwidth settings. Sweep time is adjusted to maintain a calibrated display. If a sweep time longer than the maximum available is called for, the analyzer indicates that the display is uncalibrated with a Meas Uncal message in the upper-right part of the graticule. t sweep k RBW 2 span
25 RBW and sweep time From C. Rauscher "Fundamentals of Spectrum Analysis" Rohde&Schwarz
Introduction. In the frequency domain, complex signals are separated into their frequency components, and the level at each frequency is displayed
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