第二章_离散信号频谱的窗谱校正方法(国外英文资料).docVIP

第二章_离散信号频谱的窗谱校正方法(国外英文资料) The second chapter is the spectral correction of discrete signal spectrum ---- basic theory Section 2.1 introduction DFT can be used to analyze discrete signals, but the computational effort is very large Before the invention of law, DFT didnt have much practical meaning. Until 1965, Cooley-Tukey was in the count In the journal, FFT algorithm was first proposed, DFT has been widely used. The emergence of this fast algorithm against numbers The development of signal analysis has played an important role in the field of signal analysis. Since then, it has been taken as the basis for spectrum analysis Wide range of applications [75,76,77,78] . Because the computer can calculate only a limited number of samples of the signal, the FFT spectrum analysis can only be used in time domain The signal is limited within a finite range, which inevitably results in leakage of [61] due to time domain truncation (plus rectangular window), The peak value of the spectrum is decreased, the accuracy is reduced, and the phase of the signal obtained is even beyond recognition. In digital signal processing, by DFT or The amplitude spectrum obtained by FFT is discrete spectrum, which is the signal and window function, and the frequency convolution is followed by convolution Delta FS = FS / N (FS sampling results in frequency domain such as signal sampling frequency, N for analyzing signal sample length) (Fig. Twenty-one As shown) [78]. A amplitude F Fig. 2-1 discrete spectral lines of spectral sampling If the frequency of the periodic signal is just right Table 2-1 amplitude, phase and frequency error table of discrete spectrum On a spectrum line, after FFT Rectangular window Hanning window Hamming window Amplitude error (%) 0--36.4 0--15.3 0--18.3 Phase error (0) + 90 + 90 + 90 Frequency error (Hz) + 05 Delta fs. + 05, Delta fs. + 05, Delta fs. The frequency, amplitude and phase are accurate The. In general, the signal frequency Fall between two adjacent lines, as