FFT에서 얼마나 세밀하게 분석을 하냐의 단위
동일한 파형이라도 RBW가 낮아질수록 원본에 가까운 형태로 볼 수 있는 듯.
The resolution bandwidth (RBW) determines the fast-Fourier transform (FFT) bin size, or the smallest frequency that can be resolved. |
[링크 : https://zone.ni.com/reference/en-XX/help/372058U-01/nirfsa/resolution_bandwidth/]
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2023.07.14
엇.. RBW 계산에 윈도우 타입에 따른 계수가 들어간다고..
kaiser window에서 beta1=16.7일 때 k=2.23 이라는데, k 값이 그러면 amplitude 관련 보정에 대한 그 계수가 아닌가?
Where k is a window-related coeffcient, N is the number of time-domain samples used in the DFT calculation, and Fs is the sampling frequency. For the Kaiser window with beta1 = 16.7, k is about 2.23. The RBW shape factor, defned as the frequency ratio between the spectrum amplitude at 60 dB and 3 dB, is about 4:1. On the RSA5100/6100, the spectrum analysis measurement uses Equation 2 to calculate the required number of samples for the DFT based on the input span and RBW settings. |
[링크 : https://download.tek.com/document/37W_17249_6_Fundamentals_of_Real-Time_Spectrum_Analysis1.pdf]
15p RBW의 크기에 따라 측정되는 Noise의 크기도 달라짐. Dynamic Range 확보를 위해서는 상대적으로 작은 RBW가 요구된다. |
[링크 : http://ebook.pldworld.com/_eBook/-계측기-/Rohde-Schwarz/board/스펙트럼%20분석기술%20세미나.pdf]
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2023.07.19
팔수록 이상한 용어만 잔뜩 나오냐 ㅠㅠ
RBW = (NENBW * samplerate) / input_length
인데 NENBW는 normalized effective noise bandwidth of the window 라..
RBW - Resolution bandwidth Resolution bandwidth of the estimate, returned as a scalar. The resolution bandwidth, RBW, is the smallest positive frequency, or frequency interval, that can be resolved. It is equal to NENBW*SampleRate/L, where L is the input length, and NENBW is the normalized effective noise bandwidth of the window. The data type of RBW matches the data type of the input. |
[링크 : https://kr.mathworks.com/help/dsp/ref/dsp.spectrumestimator.getrbw.html]
equivalent noise bandwidth 라는 함수인데..
이건 또 어떻게 구현되는거냐.. normalized가 빠졌는데 동일한건가?
bw = enbw(hamming(1000)) bw = 1.3638 bw = enbw(flattopwin(10000),44.1e3) bw = 16.6285 |
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