Timeline for FFT Processing Gain
Current License: CC BY-SA 3.0
18 events
| when toggle format | what | by | license | comment | |
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| Oct 5, 2023 at 6:11 | answer | added | cnMuggle | timeline score: 1 | |
| Mar 18, 2021 at 19:54 | answer | added | Hunter Akins | timeline score: 3 | |
| Nov 21, 2016 at 13:00 | history | edited | Marcus Müller |
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| Nov 17, 2016 at 22:23 | comment | added | account user | ESCHEW OBFUSCATION The FFT SNR increases with the number of FFT points because of the very DEFINITION of the FFT SNR. The FFT SNR is defined as the SNR in a BW equal to the size of the Frequency BIN and the BIN size decreases as the number of FFT points increases. | |
| Mar 9, 2016 at 11:18 | comment | added | M529 | The more samples you have, the more accurate is the frequency analysis of the FFT algorithm. Real signal adds up coherently in frequency domain, whereas noise adds up incoherently. Hence your SNR of the frequency peaks will increase if you have more samples. | |
| May 21, 2014 at 13:24 | answer | added | David | timeline score: 1 | |
| May 20, 2014 at 4:17 | history | tweeted | twitter.com/#!/StackSignals/status/468606404752715776 | ||
| May 20, 2014 at 2:40 | answer | added | learner | timeline score: 8 | |
| May 19, 2014 at 23:56 | answer | added | Phonon | timeline score: 2 | |
| May 19, 2014 at 22:07 | comment | added | Jason R | @Frank: The link provided by Seth has a slightly more detailed explanation. Increasing the FFT size to "push down the noise floor" is analogous to turning down the resolution bandwidth on a spectrum analyzer. | |
| May 19, 2014 at 21:01 | answer | added | robert bristow-johnson | timeline score: 4 | |
| May 19, 2014 at 20:36 | answer | added | hotpaw2 | timeline score: -1 | |
| May 19, 2014 at 19:41 | comment | added | random_dsp_guy | designnews.com/… | |
| May 19, 2014 at 13:06 | history | edited | Frank | CC BY-SA 3.0 |
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| May 19, 2014 at 13:06 | comment | added | Frank | I would appreciate an expanded explanation. | |
| May 19, 2014 at 12:08 | comment | added | Jason R | Short answer: one way of looking at the DFT is a uniformly-spaced bank of bandpass filters. As you increase the number of bins in your DFT, each filter has a narrower bandwidth (and therefore passes less noise). If you're searching for a narrowband signal, it pays to have the DFT bin width close to the signal of interest's bandwidth. That way, you still pass through the signal unchanged while also passing as little noise as possible. I may expand this later if I get a chance. | |
| May 19, 2014 at 9:31 | review | First posts | |||
| May 19, 2014 at 12:35 | |||||
| May 19, 2014 at 9:14 | history | asked | Frank | CC BY-SA 3.0 |