Tham khảo tài liệu 'lập trình c# all chap "numerical recipes in c" part 121', công nghệ thông tin phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Power Spectrum Estimation Using the FFT 549 f Figure . Optimal Wiener filtering. The powerspectrumof signal plus noise showsa signal peak added to a noise tail. The tail is extrapolated back into the signal region as a noise model. Subtracting gives the signal model. The models need not be accurate for the method to be useful. A simple algebraic combination of the models gives the optimal filter see text . new signal which you could improve even further with the same filtering technique. Don t waste your time on this line of thought. The scheme converges to a signal of S f 0. Converging iterative methods do exist this just isn t one of them. You can use the routine fourl or realft to FFT your data when you are constructing an optimal filter. To apply the filter to your data you can use the methods described in . The specific routine convlv is not needed for optimal filtering since your filter is constructed in the frequency domain to begin with. If you are also deconvolving your data with a known response function however you can modify convlv to multiply by your optimal filter just before it takes the inverse Fourier transform. CITED REFERENCES AND FURTHER READING Rabiner . and Gold B. 1975 TheoryandApplication of Digital SignalProcessing Englewood Cliffs NJ Prentice-Hall . Nussbaumer . 1982 FastFourier TransformandConvolutionAlgorithms New York SpringerVerlag . Elliott . and Rao . 1982 Fast Transforms Algorithms Analyses Applications New York Academic Press . Sample page from NUMERICAL RECIPES IN C THE ART OF SCIENTIFIC COMPUTING ISBN 0-521-43108-5 Power Spectrum Estimation Using the FFT In the previous section we informally estimated the power spectral density of a function c t by taking the modulus-squared of the discrete Fouriertransform of some 550 Chapter 13. Fourier and Spectral Applications finite sampled stretch of it. In this section we ll do roughly the same thing but with considerably greater attention to .
