Interpolation techniques for real-time multifrequency waveform analysis

It is well known that in discrete-Fourier-transform (DFT)-based waveform analysis of multifrequency signals, spectral parameter accuracy can be increased by windowing the time samples and interpolating the DFT coefficients. It is shown that interpolation techniques are affected very little by the number of processed samples, so that only the characteristics of the analyzed signal and the required accuracy affect the choice of this parameter. The use of a polynomial fit of some relationships reduces the processing effort and allows a greater freedom in the choice of window functions, improving accuracy and easing frequency resolution requirements