Editing Spectrum analyzer (section)

=== Realtime FFT ===
A realtime spectrum analyser does not have any blind time—up to some maximum span, often called the "realtime bandwidth". The analyser is able to sample the incoming RF spectrum in the time domain and convert the information to the frequency domain using the FFT process. FFT's are processed in parallel, gapless and overlapped so there are no gaps in the calculated RF spectrum and no information is missed.

==== Online realtime and offline realtime ====
In a sense, any spectrum analyzer that has [[vector signal analyzer]] capability is a realtime analyzer. It samples data fast enough to satisfy Nyquist Sampling theorem and stores the data in memory for later processing. This kind of analyser is only realtime for the amount of data / capture time it can store in memory and still produces gaps in the spectrum and results during processing time.

==== FFT overlapping ====
Minimizing distortion of information is important in all spectrum analyzers. The FFT process applies windowing techniques to improve the output spectrum due to producing less side lobes. The effect of windowing may also reduce the level of a signal where it is captured on the boundary between one FFT and the next. For this reason FFT's in a Realtime spectrum analyzer are overlapped. Overlapping rate is approximately 80%. An analyzer that utilises a 1024-point FFT process will re-use approximately 819 samples from the previous FFT process.<ref>''[https://www.rohde-schwarz.com/us/applications/implementation-of-real-time-spectrum-analysis-white-paper_230854-15815.html Dr. Florian Ramian – Implementation of Real-Time Spectrum Analysis] {{webarchive|url=https://web.archive.org/web/20180209182434/https://www.rohde-schwarz.com/us/applications/implementation-of-real-time-spectrum-analysis-white-paper_230854-15815.html |date=2018-02-09 }}'', p. 6, March, 2015, accessed February 9, 2018.</ref>

==== 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 10<sup>6</sup>),}} approximately every {{nowrap|20 μs.}}  This also gives us our overlap rate of 80% (20 μs − 4 μs) / 20 μs = 80%.

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

===== Persistence =====
Realtime spectrum analyzers are able to produce much more information for users to examine the frequency spectrum in more detail. A normal swept spectrum analyzer would produce max peak, min peak displays for example but a realtime spectrum analyzer is able to plot all calculated FFT's over a given period of time with the added colour-coding which represents how often a signal appears. For example, this image shows the difference between how a spectrum is displayed in a normal swept spectrum view and using a "Persistence" view on a realtime spectrum analyzer.

[[Image:Bluetooth signal behind wireless lan signal.png|thumb|right|350px|Bluetooth signal hidden behind wireless LAN signal]]

===== Hidden signals =====
Realtime spectrum analyzers are able to see signals hidden behind other signals. This is possible because no information is missed and the display to the user is the output of FFT calculations. An example of this can be seen on the right.