Spectrum analyzer: Difference between revisions

Revision as of 01:01, 13 June 2013

Revision as of 10:08, 17 June 2013

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==History==

{{expand section|date=December 2012}}

[[imageImage:sonagraphe.jpg|thumb|right|A spectrum analyzer circa 1970]]

The first spectrum analyzers, in the 1960s, were swept-tuned instruments.<ref name="Hiebert">Electronic Design

[http://electronicdesign.com/displays/take-peek-inside-todays-spectrum-analyzers], ''Take A Peek Inside Today's Spectrum Analyzers'';Bob Hiebert, 2005, accessed 10 April 2013.</ref>

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===Swept-tuned===

As discussed above in '''types''', a swept-tuned spectrum analyzer [[Superheterodyne receiver#High-side and low-side injection|down-converts]] a portion of the input signal spectrum to the center frequency of a [[band-pass filter]] by sweeping the [[voltage-controlled oscillator]] through a range of frequencies, enabling the consideration of the full frequency range of the instrument.

The bandwidth of the [[band-pass filter]] dictates the resolution bandwidth, which is related to the minimum bandwidth detectable by the instrument. As demonstrated by the animation to the right, the smaller the bandwidth, the more spectral resolution. However, there is a trade-off between how quickly the display can update the full frequency span under consideration and the frequency resolution, which is relevant for distinguishing frequency components that are close together. For a swept-tuned architecture, this relation for sweep time is useful:

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With a FFT based spectrum analyzer, the frequency resolution is <math>\Delta\nu=1/T</math>, the inverse of the time ''T'' over which the waveform is measured and Fourier transformed.

With Fourier transform analysis in a digital spectrum analyzer, it is necessary to sample the input signal with a sampling frequency <math>\nu_s</math> that is at least twice the bandwidth of the signal, due to the [[Nyquist rate|Nyquist limit]].<ref>[http://www.home.agilent.com/agilent/editorial.jspx?cc=US&lc=eng&ckey=1775376&nid=-536900125.0.00&id=1775376&pselect=SR.GENERAL How do I know what is the best sampling rate to use for my measurement?]</ref> A Fourier transform will then produce a spectrum containing all frequencies from zero to <math>\nu_s/2</math>. This can place considerable demands on the required [[analog-to-digital converter]] and processing power for the Fourier transform, making FFT based spectrum analyzers limited in frequency range.

[[Image:Aaronia Spectrum Analyzer Software.jpg|thumb|Frequency spectrum of the heating up period of a switching power supply (spread spectrum) incl. [[Waterfall plot|waterfall diagram]] over a few minutes.]]

===Hybrid superheterodyne-FFT===

Since FFT based analyzers are only capable of considering narrow bands, one technique is to combine swept and FFT analysis for consideration of wide and narrow spans. This technique allows for faster sweep time.

This method is made possible by first down converting the signal, then digitizing the [[Intermediate frequency|intermediate frequency]] and using superheterodyne or FFT techniques to acquire the spectrum.

One benefit of digitizing the intermediate frequency is the ability to use [[digital filter]]s, which have a range of [[Digital filter#Comparison of analog and digital filters|advantages]] over analog filters such as near perfect shape factors and improved filter settling time. Also, for consideration of narrow spans, the FFT can be used to increase sweep time without distorting the displayed spectrum.

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===Realtime FFT===

[[Image:Spectrum Analyser Time Domain Sampling and Blind Time.png|thumb|right|400px|Illustration showing Spectrum Analyzer Blind Time]]

[[Image:Comparison of Max Hold Spectrum Analyzer trace and Persistence Trace.png|thumb|right|400px|Comparison between Swept Max Hold and Realtime Persistence displays]]

[[Image:Bluetooth signal behind wireless lan signal.png|thumb|right|400px|Bluetooth signal hidden behind wireless LAN signal]]

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====Minimum signal detection time====

This is related to the sampling rate of the analyser and the [[Fast Fourier transform|FFT]] rate. It is also important for the realtime spectrum analyzer to give good level accuracy.

Example: for an analyser with {{nowrap|40 MHz}} of realtime [[Bandwidth (signal processing)|bandwidth]] (the maximum RF span that can be processed in realtime) approximately {{nowrap|50 Msample/second}} (complex) are needed. If the spectrum analyzer produces {{nowrap|250 000 FFT/s}} an FFT calculation is produced every {{nowrap|4 µs.}} For a {{nowrap|1024 point}} FFT a full spectrum is produced {{nowrap|1024 x (1/50 x 10⁶),}} approximately every {{nowrap|20 µs.}} This also gives us our overlap rate of 80% {{nowrap|(20 µs − 4 µs) / 20 µs &#61; 80%.}}