There's an important property of the DFT known as the shifting theorem. It states that a shift in time of a periodic x(n) input sequence manifests itself as a constant phase shift in the angles associated with the DFT results. (We won't derive the shifting theorem equation here because its derivation is included in just about every digital signal processing textbook in print.) If we decide to sample x(n) starting at n equals some integer k, as opposed to n = 0, the DFT of those time-shifted sample values is Xshifted(m) where

Equation (3-19) tells us that, if the point where we start sampling x(n) is shifted to the right by k samples, the DFT output spectrum of Xshifted(m) is X(m) with each of X(m)'s complex terms multiplied by the linear phase shift ej2pkm/N, which is merely a phase shift of 2pkm/N radians or 360km/N degrees. Conversely, if the point where we start sampling x(n) is shifted to the left by k samples, the spectrum of Xshifted(m) is X(m) multiplied by e–j2pkm/N. Let's illustrate Eq. (3-19) with an example.

3.6.1 DFT Example 2

Suppose we sampled our DFT Example 1 input sequence later in time by k = 3 samples. Figure 3-5 shows the original input time function,

Figure 3-5. Comparison of sampling times between DFT Example 1 and DFT Example 2.

We can see that Figure 3-5 is a continuation of Figure 3-2(a). Our new x(n) sequence becomes the values represented by the solid black dots in Figure 3-5 whose values are

Equation 3-20

Performing the DFT on Eq. (3-20), Xshifted(m) is

Equation 3-21

The values in Eq. (3-21) are illustrated as the dots in Figure 3-6. Notice that Figure 3-6(a) is identical to Figure 3-4(a). Equation (3-19) told us that the magnitude of Xshifted(m) should be unchanged from that of X(m). That's a comforting thought, isn't it? We wouldn't expect the DFT magnitude of our original periodic xin(t) to change just because we sampled it over a different time interval. The phase of the DFT result does, however, change depending on the instant at which we started to sample xin(t).

Figure 3-6. DFT results from Example 2: (a) magnitude of Xshifted(m); (b) phase of Xshifted(m); (c) real part of Xshifted(m); (d) imaginary part of Xshifted(m).

By looking at the m = 1 component of Xshifted(m), for example, we can double-check to see that phase values in Figure 3-6(b) are correct. Using Eq. (3-19) and remembering that X(1) from DFT Example 1 had a magnitude of 4 at a phase angle of –90 (or –p/2 radians), k = 3 and N = 8 so that

Equation 3-22

So Xshifted(1) has a magnitude of 4 and a phase angle of p/4 or +45°, which is what we set out to prove using Eq. (3-19).

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Chapter One. Discrete Sequences and Systems

Chapter Two. Periodic Sampling

Chapter Three. The Discrete Fourier Transform

Chapter Four. The Fast Fourier Transform

Chapter Five. Finite Impulse Response Filters

Chapter Six. Infinite Impulse Response Filters

Chapter Seven. Specialized Lowpass FIR Filters

Chapter Eight. Quadrature Signals

Chapter Nine. The Discrete Hilbert Transform

Chapter Ten. Sample Rate Conversion

Chapter Eleven. Signal Averaging

Chapter Twelve. Digital Data Formats and Their Effects

Chapter Thirteen. Digital Signal Processing Tricks

Appendix A. The Arithmetic of Complex Numbers

Appendix B. Closed Form of a Geometric Series

Appendix C. Time Reversal and the DFT

Appendix D. Mean, Variance, and Standard Deviation

Appendix E. Decibels (dB and dBm)

Appendix F. Digital Filter Terminology

Appendix G. Frequency Sampling Filter Derivations

Appendix H. Frequency Sampling Filter Design Tables

Understanding Digital Signal Processing
Understanding Digital Signal Processing (2nd Edition)
ISBN: 0131089897
EAN: 2147483647
Year: 2004
Pages: 183

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