[PDF] Exact Signal Measurements using FFT Analysis - KLUEDO

(first) DC bin This results in a RMS valued FFT representing the RMS spectrum depends on signal frequency, sample rate and the number of bins However

The Fast Fourier Transform (FFT) and the power spectrum are powerful tools for Frequency lines also can be referred to as frequency bins or FFT bins

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between two discrete spectrum bins This error may be interpreted as the resolution of an FFT frequency measurement method Denoting the largest frequency

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5 fév 2015 · When taking the FFT of a signal, the size of the transform is equivalent to the number of frequency bins that will be created Each bin represents

[PDF] Exact Signal Measurements using FFT Analysis - KLUEDO - TU

(first) DC bin This results in a RMS valued FFT representing the RMS spectrum depends on signal frequency, sample rate and the number of bins However

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there are N/2 frequency bins The distance in Hz between the center frequencies of two neighboring bins is always the same: ∆f Power (Density) Spectrum

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The change in frequency from one bin to the next is shown by equation 1 1 1 2 FFT Frequency Range The range of frequencies covered in the output record

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in the periodic signal processed by the FFT, causing the energy contained in the signal to 'leak' from the signal frequency bin into adjacent frequency bins

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Energy values present in the frequency bins represent the amount of that the frequency present in the waveform The bin with the largest magnitude is usually, but

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time domain signal complex FFTbinsRMSspectrumpower spectrumpower spectrum x 10 7 -140 -120 -100 -80 -60 -40 -20 0

256 point FFT, leakage effects, 50 MHz signal with 0 dBm, fs=250 MHz

frequency in Hz power in dBm no window hann window flattop window blackman window ??? ????? ??????? ?? ??? ??????? ?? ????? ?? ???? ????012345 x 10 -7 -1 -0.5 0 0.5 1 time domain: no windowing (rectangle) time in s amplitude in volts

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x 10 -7 -1 -0.5 0 0.5 1 time domain: hann window time in s

4.94.924.944.964.9855.025.045.065.085.1frequency in Hz

?? ????? ??????? ???? ???? ?????024681012frequency in Hz

107-50-40-30-20-100power in dBmsmall scalloping loss, signal power: 0dBm16 point FFTtrue spectrum

(here including leakage)024681012frequency in Hz

107-50-40-30-20-100power in dBmlarge scalloping loss, signal power: 0 dBm16 point FFTtrue spectrum

PG[dB] = 10log10N2

107-100-80-60-40-200power in dBmnoisy sine signal with different FFT sizes (hann window)256 point1024 point65k point024681012frequency in Hz

x 10 7-150 -100 -50 0

Overview of errors in FFT

frequency in Hz power in dBm coherent power gain & scalloping loss equivalent noise bandwidth & processing gain P true[dBm] =Pdispl[dBm] +CPG[dB] +scalloping loss[dB]????? P true[dBm] =Pfloor[dBm] +CPG[dB] +PG[dB]ENBWcorr[dB]????? P true[dBm] = 10log10 X iFFT(i)[linear]! +CPG[dB]ENBWcorr[dB] ?????? ??? ?? ???? ????Pdispl= 5:9dBm? ????? ??? ??? ??? ???? ????? ?? ??? ???? ?????? ?? P ??? ???? ?? ???????1:8dB? ?13:02dBm????? ???????024681012 x 10 7-25 -20 -15 -10 -5 0 5 10

[PDF] [PDF] Exact Signal Measurements using FFT Analysis - KLUEDO - TU

[PDF] The Fundamentals of FFT-Based Signal Analysis and Measurement