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外文题目： Fourier Transform Pairs
2017年4月20日
Fourier Transform Pairs
For every time domain waveform there is a corresponding frequency domain waveform, and vice versa. For example, a rectangular pulse in the time domain coincides with a sinc function [i.e.,sin(x)/x] in the frequency domain. Duality provides that the reverse is also true; a rectangular pulse in the frequency domain matches a sinc function in the time domain. Waveforms that correspond to each other in this manner are called Fourier transform pairs. Several common pairs are presented in this chapter.
Delta Function Pairs
For discrete signals, the delta function is a simple waveform, and has an equally simple Fourier transform pair. Figure 11一1 a shows a delta function in the time domain, with its frequency spectrum in (b) and (c). The magnitude is a constant value, while the phase is entirely zero. As discussed in the last chapter, this can be understood by using the expansionJcompression property.When the time domain is compressed until it becomes an impulse, the frequency domain is expanded until it becomes a constant value.
In(d) and (g), the time domain waveform is shifted four and eight samples to the right, respectively. As expected from the properties in the last chapter,shifting the time domain waveform does not affect the magnitude, but adds a linear component to the phase. The phase signals in this figure have not been unwrapped, and thus extend only from -n to s}. Also notice that the horizontal axes in the frequency domain run from }.5 to 0.5. That is, they show the negative frequencies in the spectrum, as well as the positive ones. The negative frequencies arc redundant information, but they are often included in DSP graphs and you should become accustomed to seeing them.
Figure 11-2 presents the same information as Fig. 11一1，but with the frequency domain in rectangular form. There are two lessons to be learned here. First, compare the polar and rectangular representations of the
frequency domains. As is usually the case, the polar form is much easier to understand; the magnitude is nothing more than a constant, while the phase is a straight line. In comparison, the real and imaginary parts are sinusoidal oscillations that are difficult to attach a meaning to.
The second interesting feature in Fig. 11-2 is the duality of the DFT. In the conventional view, each sample in the DFTs frequency domain corresponds to a sinusoid in the time domain. However, the reverse of this is also true:each sample in the time domain corresponds to sinusoids in the frequencydomain. Including the negative frequencies in these graphs allows theduality property to be more symmetrical. For instance, Figs. (d), (e), and
(f）show that an impulse at sample number four in the time domain results in four cycles of a cosine wave in the real part of the frequency spectrum, and four cycles of a negative sine wave in the imaginary part. As you recall, an impulse at sample number four in the real part of the frequency spectrum results in four cycles of a cosine wave in the time domain. Likewise, an impulse at sample number four in the imaginary part of the frequency spectrum results in four cycles of a negative sine wave being added to the time domainwave.
As mentioned in Chapter 8, this can be used as another way to calculate the DFT (besides correlating the time domain with sinusoids). Each sample in the time domain results in a cosine wave being added to the real part of the frequency domain, and a negative sine wave being added to the imaginary part.The amplatude of each sinusoid is given by the amplitude of the time domain sample. The equency of each sinusoid is provided by the sample number of the time domain point. The algorithm involves: (1) stepping through each time domain sample, (2) calculating the sine and cosine waves that correspond to each sample, and (3) adding up all of the contributing sinusoids. The resulting program is nearly identical to the correlation method (Table 8-2), except thatthe outer and inner loops are exchanged.
The Sinc Function
Figure 11-4 illustrates a common transform pair: the rectangular pulse and the sinc function (pronounced 'sink'). The sinc function is defined as:sinc(a)=sin(}a)/(tea); however, it is common to see the vague statement: 'the sinc function is of the general form: sin(x)/x.' In other words, the sinc is a sine wave that decays in amplitude as 1/x. In (a), the rectangular pulse is symmetrically centered on sample zero, making one-half of the pulse on the right of the graph and the other one-half on the left. This appears to the DFT as a single pulse because of the time domain periodicity. The DFT of this signal is shown in (b) and (c), with the unwrapped version in (d) and (e).
First look at the unwrapped spectrum, (d) and (e).The unwrapped magnitude is an oscillation that decreases in amplitude with increasing frequency.The phase is composed of all zeros, as you should expect for a time domain signal that is symmetrical around sample number zero. We are using the term unwrapped magnitude to indicate that it can have bothpositive and negative values. By definition, the magnitude must always be positive.This is shown in (b) and (c) where the magnitude is made all positive by introducing a phase shift of n at all frequencies where the unwrapped magnitude is negative in (d).
In(f),the signal is shifted so that it appears as one contiguous pulse, but is no longer centered on sample number zero. While this doesnt change the magnitude of the frequency domain, it does add a linear component to the phase, making it a jumbl
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