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# Complex Fourier Transform Notes | EduRev

## : Complex Fourier Transform Notes | EduRev

``` Page 1

567
CHAPTER
31
Re X [ k ] '
2 N j
N & 1 n ' 0 x [ n ] cos ( 2 B k n / N ) Im X [ k ] '
& 2 N j
N & 1 n ' 0 x [ n ] sin ( 2 B k n / N ) EQUATION 31-1
The real DFT.  This is the forward transform,
calculating the frequency domain from the
time domain. In spite of using the names: real
part and imaginary part , these equations
only involve ordinary numbers.  The
frequency index, k , runs from 0 to N /2. These
are the same equations given in Eq. 8-4,
except that the  2/ N term has been included in
the forward transform.
The Complex Fourier Transform
Although complex numbers are fundamentally disconnected from our reality, they can be used to
solve science and engineering problems in two ways.  First, the parameters from a real world
problem can be substituted into a complex form, as presented in the last chapter.  The second
method is much more elegant and powerful, a way of making the complex numbers
mathematically equivalent to the physical problem.  This approach leads to the complex Fourier
transform , a more sophisticated version of the real Fourier transform discussed in Chapter 8.
The complex Fourier transform is important in itself, but also as a stepping stone to more
powerful complex techniques, such as the Laplace and z-transforms . These complex transforms
are the foundation of theoretical DSP.
The Real DFT
All four members of the Fourier transform family (DFT, DTFT, Fourier
Transform & Fourier Series) can be carried out with either real numbers or
complex numbers.  Since DSP is mainly concerned with the DFT, we will use
it as an example.   Before jumping into the complex math, let's review the real
DFT with a special emphasis on things that are awkward with the mathematics.
In Chapter 8 we defined the real version of the Discrete Fourier Transform
according to the equations:
In words, an N sample time domain signal, , is decomposed into a set x [ n ] of  cosine waves, and  sine waves, with frequencies given by the N / 2 % 1 N / 2 % 1
Page 2

567
CHAPTER
31
Re X [ k ] '
2 N j
N & 1 n ' 0 x [ n ] cos ( 2 B k n / N ) Im X [ k ] '
& 2 N j
N & 1 n ' 0 x [ n ] sin ( 2 B k n / N ) EQUATION 31-1
The real DFT.  This is the forward transform,
calculating the frequency domain from the
time domain. In spite of using the names: real
part and imaginary part , these equations
only involve ordinary numbers.  The
frequency index, k , runs from 0 to N /2. These
are the same equations given in Eq. 8-4,
except that the  2/ N term has been included in
the forward transform.
The Complex Fourier Transform
Although complex numbers are fundamentally disconnected from our reality, they can be used to
solve science and engineering problems in two ways.  First, the parameters from a real world
problem can be substituted into a complex form, as presented in the last chapter.  The second
method is much more elegant and powerful, a way of making the complex numbers
mathematically equivalent to the physical problem.  This approach leads to the complex Fourier
transform , a more sophisticated version of the real Fourier transform discussed in Chapter 8.
The complex Fourier transform is important in itself, but also as a stepping stone to more
powerful complex techniques, such as the Laplace and z-transforms . These complex transforms
are the foundation of theoretical DSP.
The Real DFT
All four members of the Fourier transform family (DFT, DTFT, Fourier
Transform & Fourier Series) can be carried out with either real numbers or
complex numbers.  Since DSP is mainly concerned with the DFT, we will use
it as an example.   Before jumping into the complex math, let's review the real
DFT with a special emphasis on things that are awkward with the mathematics.
In Chapter 8 we defined the real version of the Discrete Fourier Transform
according to the equations:
In words, an N sample time domain signal, , is decomposed into a set x [ n ] of  cosine waves, and  sine waves, with frequencies given by the N / 2 % 1 N / 2 % 1 The Scientist and Engineer's Guide to Digital Signal Processing 568
index, k .  The amplitudes of the cosine waves are contained in , while Re X [ k ] the amplitudes of the sine waves are contained in . These equations Im X [ k ] operate by correlating the respective cosine or sine wave with the time domain
signal.  In spite of using the names: real part and imaginary part , there are no
complex numbers in these equations.  There isn't a j anywhere in sight!  We
have also included the normalization factor,   in these equations. 2 / N Remember, this can be placed in front of either the synthesis or analysis
equation, or be handled as a separate step (as described by Eq. 8-3). These
equations should be very familiar from previous chapters.  If they aren't, go
back and brush up on these concepts before continuing.  If you don't understand
the real DFT, you will never be able to understand the complex DFT.
Even though the real DFT uses only real numbers, substitution allows the
frequency domain to be represented using complex numbers.  As suggested by
the names of the arrays,  becomes the real part of the complex Re X [ k ] frequency spectrum, and  becomes the imaginary part.  In other words, Im X [ k ] we place a j with each value in the imaginary part, and add the result to the
real part.  However, do not make the mistake of thinking that this is the
"complex DFT."  This is nothing more than the real DFT with complex
substitution .

While the real DFT is adequate for many applications in science and
engineering, it is mathematically awkward in three respects.  First, it can only
take advantage of complex numbers through the use of substitution . This
makes mathematicians uncomfortable; they want to say: "this equals that," not
simply: "this represents that."  For instance, imagine we are given the
mathematical statement: A equals B .  We immediately know countless
consequences: , , , etc.  Now suppose we are 5 A ' 5 B 1 % A ' 1 % B A / x ' B / x given the statement: A represents B .  Without additional information, we know
years of mathematics.  When things only represent each other, we must start
from scratch with new definitions.  For example, when sinusoids are
represented by complex numbers, we allow addition and subtraction, but
prohibit multiplication and division.
The second thing handled poorly by the real Fourier transform is the  negative
frequency portion of the spectrum.  As you recall from Chapter 10,  sine and
cosine waves can be described as having a positive frequency or a negative
frequency.  Since the two views are identical, the real Fourier transform
ignores the negative frequencies.  However, there are applications where the
negative frequencies are important.   This occurs when negative frequency
components are forced to move into the positive frequency portion of the
spectrum.  The ghosts take human form, so to speak.  For instance, this is what
happens in aliasing, circular convolution, and amplitude modulation. Since the
real Fourier transform doesn't use negative frequencies, its ability to deal with
these situations is very limited.
Our third complaint is the special handing of  and , the Re X [ 0 ] Re X [ N / 2 ] first and last points in the frequency spectrum.  Suppose we start with an N
Page 3

567
CHAPTER
31
Re X [ k ] '
2 N j
N & 1 n ' 0 x [ n ] cos ( 2 B k n / N ) Im X [ k ] '
& 2 N j
N & 1 n ' 0 x [ n ] sin ( 2 B k n / N ) EQUATION 31-1
The real DFT.  This is the forward transform,
calculating the frequency domain from the
time domain. In spite of using the names: real
part and imaginary part , these equations
only involve ordinary numbers.  The
frequency index, k , runs from 0 to N /2. These
are the same equations given in Eq. 8-4,
except that the  2/ N term has been included in
the forward transform.
The Complex Fourier Transform
Although complex numbers are fundamentally disconnected from our reality, they can be used to
solve science and engineering problems in two ways.  First, the parameters from a real world
problem can be substituted into a complex form, as presented in the last chapter.  The second
method is much more elegant and powerful, a way of making the complex numbers
mathematically equivalent to the physical problem.  This approach leads to the complex Fourier
transform , a more sophisticated version of the real Fourier transform discussed in Chapter 8.
The complex Fourier transform is important in itself, but also as a stepping stone to more
powerful complex techniques, such as the Laplace and z-transforms . These complex transforms
are the foundation of theoretical DSP.
The Real DFT
All four members of the Fourier transform family (DFT, DTFT, Fourier
Transform & Fourier Series) can be carried out with either real numbers or
complex numbers.  Since DSP is mainly concerned with the DFT, we will use
it as an example.   Before jumping into the complex math, let's review the real
DFT with a special emphasis on things that are awkward with the mathematics.
In Chapter 8 we defined the real version of the Discrete Fourier Transform
according to the equations:
In words, an N sample time domain signal, , is decomposed into a set x [ n ] of  cosine waves, and  sine waves, with frequencies given by the N / 2 % 1 N / 2 % 1 The Scientist and Engineer's Guide to Digital Signal Processing 568
index, k .  The amplitudes of the cosine waves are contained in , while Re X [ k ] the amplitudes of the sine waves are contained in . These equations Im X [ k ] operate by correlating the respective cosine or sine wave with the time domain
signal.  In spite of using the names: real part and imaginary part , there are no
complex numbers in these equations.  There isn't a j anywhere in sight!  We
have also included the normalization factor,   in these equations. 2 / N Remember, this can be placed in front of either the synthesis or analysis
equation, or be handled as a separate step (as described by Eq. 8-3). These
equations should be very familiar from previous chapters.  If they aren't, go
back and brush up on these concepts before continuing.  If you don't understand
the real DFT, you will never be able to understand the complex DFT.
Even though the real DFT uses only real numbers, substitution allows the
frequency domain to be represented using complex numbers.  As suggested by
the names of the arrays,  becomes the real part of the complex Re X [ k ] frequency spectrum, and  becomes the imaginary part.  In other words, Im X [ k ] we place a j with each value in the imaginary part, and add the result to the
real part.  However, do not make the mistake of thinking that this is the
"complex DFT."  This is nothing more than the real DFT with complex
substitution .

While the real DFT is adequate for many applications in science and
engineering, it is mathematically awkward in three respects.  First, it can only
take advantage of complex numbers through the use of substitution . This
makes mathematicians uncomfortable; they want to say: "this equals that," not
simply: "this represents that."  For instance, imagine we are given the
mathematical statement: A equals B .  We immediately know countless
consequences: , , , etc.  Now suppose we are 5 A ' 5 B 1 % A ' 1 % B A / x ' B / x given the statement: A represents B .  Without additional information, we know
years of mathematics.  When things only represent each other, we must start
from scratch with new definitions.  For example, when sinusoids are
represented by complex numbers, we allow addition and subtraction, but
prohibit multiplication and division.
The second thing handled poorly by the real Fourier transform is the  negative
frequency portion of the spectrum.  As you recall from Chapter 10,  sine and
cosine waves can be described as having a positive frequency or a negative
frequency.  Since the two views are identical, the real Fourier transform
ignores the negative frequencies.  However, there are applications where the
negative frequencies are important.   This occurs when negative frequency
components are forced to move into the positive frequency portion of the
spectrum.  The ghosts take human form, so to speak.  For instance, this is what
happens in aliasing, circular convolution, and amplitude modulation. Since the
real Fourier transform doesn't use negative frequencies, its ability to deal with
these situations is very limited.
Our third complaint is the special handing of  and , the Re X [ 0 ] Re X [ N / 2 ] first and last points in the frequency spectrum.  Suppose we start with an N Chapter 31- The Complex Fourier Transform 569
EQUATION 31-2
Euler's relation.
e j x ' cos ( x ) % j sin ( x ) EQUATION 31-3
Euler's relation for
sine & cosine.
sin ( x ) '
e j x & e & j x 2 j cos ( x ) '
e j x % e & j x 2 sin ( Tt ) '
1 2 j e j ( &T ) t &
1 2 j e j T t EQUATION 31-4
Sinusoids as complex numbers. Using
complex numbers, cosine and sine waves
can be written as the sum of a positive
and a negative frequency.
cos ( Tt ) '
1 2 e j ( &T ) t %
1 2 e j T t point signal, .  Taking the DFT provides the frequency spectrum contained x [ n ] in  and , where k runs from 0 to N /2.  However, these are not Re X [ k ] Im X [ k ] the amplitudes needed to reconstruct the time domain waveform; samples
and  must first be divided by two.  (See Eq. 8-3 to refresh Re X [ 0 ] Re X [ N / 2 ] your memory).  This is easily carried out in computer programs, but
inconvenient to deal with in equations.
The complex Fourier transform is an elegant solution to these problems.  It is
natural for complex numbers and negative frequencies to go hand-in-hand.
Let's see how it works.
Mathematical Equivalence
Our first step is to show how sine and cosine waves can be written in an
equation with complex numbers.  The key to this is Euler's relation, presented
in the last chapter:
At first glance, this doesn't appear to be much help; one complex expression is
equal to another complex expression.  Nevertheless, a little algebra can
rearrange the relation into two other forms:
This result is extremely important, we have developed a way of writing
equations between complex numbers and ordinary sinusoids.  Although Eq. 31-
3 is the standard form of the identity, it will be more useful for this discussion
if we change a few terms around:
Each expression is the sum of two exponentials: one containing a positive
frequency ( T ), and the other containing a negative frequency (- T ).   In other
words, when sine and cosine waves are written as complex numbers, the
Page 4

567
CHAPTER
31
Re X [ k ] '
2 N j
N & 1 n ' 0 x [ n ] cos ( 2 B k n / N ) Im X [ k ] '
& 2 N j
N & 1 n ' 0 x [ n ] sin ( 2 B k n / N ) EQUATION 31-1
The real DFT.  This is the forward transform,
calculating the frequency domain from the
time domain. In spite of using the names: real
part and imaginary part , these equations
only involve ordinary numbers.  The
frequency index, k , runs from 0 to N /2. These
are the same equations given in Eq. 8-4,
except that the  2/ N term has been included in
the forward transform.
The Complex Fourier Transform
Although complex numbers are fundamentally disconnected from our reality, they can be used to
solve science and engineering problems in two ways.  First, the parameters from a real world
problem can be substituted into a complex form, as presented in the last chapter.  The second
method is much more elegant and powerful, a way of making the complex numbers
mathematically equivalent to the physical problem.  This approach leads to the complex Fourier
transform , a more sophisticated version of the real Fourier transform discussed in Chapter 8.
The complex Fourier transform is important in itself, but also as a stepping stone to more
powerful complex techniques, such as the Laplace and z-transforms . These complex transforms
are the foundation of theoretical DSP.
The Real DFT
All four members of the Fourier transform family (DFT, DTFT, Fourier
Transform & Fourier Series) can be carried out with either real numbers or
complex numbers.  Since DSP is mainly concerned with the DFT, we will use
it as an example.   Before jumping into the complex math, let's review the real
DFT with a special emphasis on things that are awkward with the mathematics.
In Chapter 8 we defined the real version of the Discrete Fourier Transform
according to the equations:
In words, an N sample time domain signal, , is decomposed into a set x [ n ] of  cosine waves, and  sine waves, with frequencies given by the N / 2 % 1 N / 2 % 1 The Scientist and Engineer's Guide to Digital Signal Processing 568
index, k .  The amplitudes of the cosine waves are contained in , while Re X [ k ] the amplitudes of the sine waves are contained in . These equations Im X [ k ] operate by correlating the respective cosine or sine wave with the time domain
signal.  In spite of using the names: real part and imaginary part , there are no
complex numbers in these equations.  There isn't a j anywhere in sight!  We
have also included the normalization factor,   in these equations. 2 / N Remember, this can be placed in front of either the synthesis or analysis
equation, or be handled as a separate step (as described by Eq. 8-3). These
equations should be very familiar from previous chapters.  If they aren't, go
back and brush up on these concepts before continuing.  If you don't understand
the real DFT, you will never be able to understand the complex DFT.
Even though the real DFT uses only real numbers, substitution allows the
frequency domain to be represented using complex numbers.  As suggested by
the names of the arrays,  becomes the real part of the complex Re X [ k ] frequency spectrum, and  becomes the imaginary part.  In other words, Im X [ k ] we place a j with each value in the imaginary part, and add the result to the
real part.  However, do not make the mistake of thinking that this is the
"complex DFT."  This is nothing more than the real DFT with complex
substitution .

While the real DFT is adequate for many applications in science and
engineering, it is mathematically awkward in three respects.  First, it can only
take advantage of complex numbers through the use of substitution . This
makes mathematicians uncomfortable; they want to say: "this equals that," not
simply: "this represents that."  For instance, imagine we are given the
mathematical statement: A equals B .  We immediately know countless
consequences: , , , etc.  Now suppose we are 5 A ' 5 B 1 % A ' 1 % B A / x ' B / x given the statement: A represents B .  Without additional information, we know
years of mathematics.  When things only represent each other, we must start
from scratch with new definitions.  For example, when sinusoids are
represented by complex numbers, we allow addition and subtraction, but
prohibit multiplication and division.
The second thing handled poorly by the real Fourier transform is the  negative
frequency portion of the spectrum.  As you recall from Chapter 10,  sine and
cosine waves can be described as having a positive frequency or a negative
frequency.  Since the two views are identical, the real Fourier transform
ignores the negative frequencies.  However, there are applications where the
negative frequencies are important.   This occurs when negative frequency
components are forced to move into the positive frequency portion of the
spectrum.  The ghosts take human form, so to speak.  For instance, this is what
happens in aliasing, circular convolution, and amplitude modulation. Since the
real Fourier transform doesn't use negative frequencies, its ability to deal with
these situations is very limited.
Our third complaint is the special handing of  and , the Re X [ 0 ] Re X [ N / 2 ] first and last points in the frequency spectrum.  Suppose we start with an N Chapter 31- The Complex Fourier Transform 569
EQUATION 31-2
Euler's relation.
e j x ' cos ( x ) % j sin ( x ) EQUATION 31-3
Euler's relation for
sine & cosine.
sin ( x ) '
e j x & e & j x 2 j cos ( x ) '
e j x % e & j x 2 sin ( Tt ) '
1 2 j e j ( &T ) t &
1 2 j e j T t EQUATION 31-4
Sinusoids as complex numbers. Using
complex numbers, cosine and sine waves
can be written as the sum of a positive
and a negative frequency.
cos ( Tt ) '
1 2 e j ( &T ) t %
1 2 e j T t point signal, .  Taking the DFT provides the frequency spectrum contained x [ n ] in  and , where k runs from 0 to N /2.  However, these are not Re X [ k ] Im X [ k ] the amplitudes needed to reconstruct the time domain waveform; samples
and  must first be divided by two.  (See Eq. 8-3 to refresh Re X [ 0 ] Re X [ N / 2 ] your memory).  This is easily carried out in computer programs, but
inconvenient to deal with in equations.
The complex Fourier transform is an elegant solution to these problems.  It is
natural for complex numbers and negative frequencies to go hand-in-hand.
Let's see how it works.
Mathematical Equivalence
Our first step is to show how sine and cosine waves can be written in an
equation with complex numbers.  The key to this is Euler's relation, presented
in the last chapter:
At first glance, this doesn't appear to be much help; one complex expression is
equal to another complex expression.  Nevertheless, a little algebra can
rearrange the relation into two other forms:
This result is extremely important, we have developed a way of writing
equations between complex numbers and ordinary sinusoids.  Although Eq. 31-
3 is the standard form of the identity, it will be more useful for this discussion
if we change a few terms around:
Each expression is the sum of two exponentials: one containing a positive
frequency ( T ), and the other containing a negative frequency (- T ).   In other
words, when sine and cosine waves are written as complex numbers, the
The Scientist and Engineer's Guide to Digital Signal Processing 570
EQUATION 31-5
The forward complex DFT.  Both the
time domain, , and the frequency x [ n ] domain, , are arrays of complex X [ k ] numbers, with k and n running from 0
to N -1. This equation is in polar form,
the most common for DSP.
X [ k ] '
1 N j
N & 1 n ' 0 x [ n ] e & j 2 B k n / N X [ k ] '
1 N j
N & 1 n ' 0 x [ n ] cos ( 2 B k n / N ) & j sin ( 2 B k n / N ) EQUATION 31-6
The forward complex DFT
(rectangular form).
negative portion of the frequency spectrum is automatically included.  The
positive and negative frequencies are treated with an equal status; it requires
one-half of each to form a complete waveform.
The Complex DFT
The forward complex DFT, written in polar form, is given by:
Alternatively, Euler's relation can be used to rewrite the forward transform in
rectangular form:
To start, compare this equation of the complex Fourier transform with the
equation of the real Fourier transform , Eq. 31-1.  At first glance, they appear
to be identical, with only small amount of algebra being required to turn Eq.
31-6 into Eq. 31-1.  However, this is very misleading; the differences between
these two equations are very subtle and easy to overlook, but tremendously
important.   Let's go through the differences in detail.
First, the real Fourier transform converts a real time domain signal, , into x [ n ] two real frequency domain signals,  & .  By using complex Re X [ k ] Im X [ k ] substitution, the frequency domain can be represented by a single complex
array, .  In the complex Fourier transform, both  &  are arrays X [ k ] x [ n ] X [ k ] of complex numbers.  A practical note:  Even though the time domain is
complex, there is nothing that requires us to use the imaginary part.  Suppose
we want to process a real signal, such as a series of voltage measurements
taken over time.  This group of data becomes the real part of the time domain
signal, while the imaginary part is composed of zeros.
Second, the real Fourier transform only deals with positive frequencies.
That is, the frequency domain index, k , only runs from 0 to N /2.  In
comparison, the complex Fourier transform includes both positive and
negative frequencies.  This means k runs from 0 to N -1.  The frequencies
between 0 and N /2 are positive, while the frequencies between N /2 and N -1
are negative.  Remember, the frequency spectrum of a discrete signal is
periodic, making the negative frequencies between N /2 and N -1 the same as
Page 5

567
CHAPTER
31
Re X [ k ] '
2 N j
N & 1 n ' 0 x [ n ] cos ( 2 B k n / N ) Im X [ k ] '
& 2 N j
N & 1 n ' 0 x [ n ] sin ( 2 B k n / N ) EQUATION 31-1
The real DFT.  This is the forward transform,
calculating the frequency domain from the
time domain. In spite of using the names: real
part and imaginary part , these equations
only involve ordinary numbers.  The
frequency index, k , runs from 0 to N /2. These
are the same equations given in Eq. 8-4,
except that the  2/ N term has been included in
the forward transform.
The Complex Fourier Transform
Although complex numbers are fundamentally disconnected from our reality, they can be used to
solve science and engineering problems in two ways.  First, the parameters from a real world
problem can be substituted into a complex form, as presented in the last chapter.  The second
method is much more elegant and powerful, a way of making the complex numbers
mathematically equivalent to the physical problem.  This approach leads to the complex Fourier
transform , a more sophisticated version of the real Fourier transform discussed in Chapter 8.
The complex Fourier transform is important in itself, but also as a stepping stone to more
powerful complex techniques, such as the Laplace and z-transforms . These complex transforms
are the foundation of theoretical DSP.
The Real DFT
All four members of the Fourier transform family (DFT, DTFT, Fourier
Transform & Fourier Series) can be carried out with either real numbers or
complex numbers.  Since DSP is mainly concerned with the DFT, we will use
it as an example.   Before jumping into the complex math, let's review the real
DFT with a special emphasis on things that are awkward with the mathematics.
In Chapter 8 we defined the real version of the Discrete Fourier Transform
according to the equations:
In words, an N sample time domain signal, , is decomposed into a set x [ n ] of  cosine waves, and  sine waves, with frequencies given by the N / 2 % 1 N / 2 % 1 The Scientist and Engineer's Guide to Digital Signal Processing 568
index, k .  The amplitudes of the cosine waves are contained in , while Re X [ k ] the amplitudes of the sine waves are contained in . These equations Im X [ k ] operate by correlating the respective cosine or sine wave with the time domain
signal.  In spite of using the names: real part and imaginary part , there are no
complex numbers in these equations.  There isn't a j anywhere in sight!  We
have also included the normalization factor,   in these equations. 2 / N Remember, this can be placed in front of either the synthesis or analysis
equation, or be handled as a separate step (as described by Eq. 8-3). These
equations should be very familiar from previous chapters.  If they aren't, go
back and brush up on these concepts before continuing.  If you don't understand
the real DFT, you will never be able to understand the complex DFT.
Even though the real DFT uses only real numbers, substitution allows the
frequency domain to be represented using complex numbers.  As suggested by
the names of the arrays,  becomes the real part of the complex Re X [ k ] frequency spectrum, and  becomes the imaginary part.  In other words, Im X [ k ] we place a j with each value in the imaginary part, and add the result to the
real part.  However, do not make the mistake of thinking that this is the
"complex DFT."  This is nothing more than the real DFT with complex
substitution .

While the real DFT is adequate for many applications in science and
engineering, it is mathematically awkward in three respects.  First, it can only
take advantage of complex numbers through the use of substitution . This
makes mathematicians uncomfortable; they want to say: "this equals that," not
simply: "this represents that."  For instance, imagine we are given the
mathematical statement: A equals B .  We immediately know countless
consequences: , , , etc.  Now suppose we are 5 A ' 5 B 1 % A ' 1 % B A / x ' B / x given the statement: A represents B .  Without additional information, we know
years of mathematics.  When things only represent each other, we must start
from scratch with new definitions.  For example, when sinusoids are
represented by complex numbers, we allow addition and subtraction, but
prohibit multiplication and division.
The second thing handled poorly by the real Fourier transform is the  negative
frequency portion of the spectrum.  As you recall from Chapter 10,  sine and
cosine waves can be described as having a positive frequency or a negative
frequency.  Since the two views are identical, the real Fourier transform
ignores the negative frequencies.  However, there are applications where the
negative frequencies are important.   This occurs when negative frequency
components are forced to move into the positive frequency portion of the
spectrum.  The ghosts take human form, so to speak.  For instance, this is what
happens in aliasing, circular convolution, and amplitude modulation. Since the
real Fourier transform doesn't use negative frequencies, its ability to deal with
these situations is very limited.
Our third complaint is the special handing of  and , the Re X [ 0 ] Re X [ N / 2 ] first and last points in the frequency spectrum.  Suppose we start with an N Chapter 31- The Complex Fourier Transform 569
EQUATION 31-2
Euler's relation.
e j x ' cos ( x ) % j sin ( x ) EQUATION 31-3
Euler's relation for
sine & cosine.
sin ( x ) '
e j x & e & j x 2 j cos ( x ) '
e j x % e & j x 2 sin ( Tt ) '
1 2 j e j ( &T ) t &
1 2 j e j T t EQUATION 31-4
Sinusoids as complex numbers. Using
complex numbers, cosine and sine waves
can be written as the sum of a positive
and a negative frequency.
cos ( Tt ) '
1 2 e j ( &T ) t %
1 2 e j T t point signal, .  Taking the DFT provides the frequency spectrum contained x [ n ] in  and , where k runs from 0 to N /2.  However, these are not Re X [ k ] Im X [ k ] the amplitudes needed to reconstruct the time domain waveform; samples
and  must first be divided by two.  (See Eq. 8-3 to refresh Re X [ 0 ] Re X [ N / 2 ] your memory).  This is easily carried out in computer programs, but
inconvenient to deal with in equations.
The complex Fourier transform is an elegant solution to these problems.  It is
natural for complex numbers and negative frequencies to go hand-in-hand.
Let's see how it works.
Mathematical Equivalence
Our first step is to show how sine and cosine waves can be written in an
equation with complex numbers.  The key to this is Euler's relation, presented
in the last chapter:
At first glance, this doesn't appear to be much help; one complex expression is
equal to another complex expression.  Nevertheless, a little algebra can
rearrange the relation into two other forms:
This result is extremely important, we have developed a way of writing
equations between complex numbers and ordinary sinusoids.  Although Eq. 31-
3 is the standard form of the identity, it will be more useful for this discussion
if we change a few terms around:
Each expression is the sum of two exponentials: one containing a positive
frequency ( T ), and the other containing a negative frequency (- T ).   In other
words, when sine and cosine waves are written as complex numbers, the
The Scientist and Engineer's Guide to Digital Signal Processing 570
EQUATION 31-5
The forward complex DFT.  Both the
time domain, , and the frequency x [ n ] domain, , are arrays of complex X [ k ] numbers, with k and n running from 0
to N -1. This equation is in polar form,
the most common for DSP.
X [ k ] '
1 N j
N & 1 n ' 0 x [ n ] e & j 2 B k n / N X [ k ] '
1 N j
N & 1 n ' 0 x [ n ] cos ( 2 B k n / N ) & j sin ( 2 B k n / N ) EQUATION 31-6
The forward complex DFT
(rectangular form).
negative portion of the frequency spectrum is automatically included.  The
positive and negative frequencies are treated with an equal status; it requires
one-half of each to form a complete waveform.
The Complex DFT
The forward complex DFT, written in polar form, is given by:
Alternatively, Euler's relation can be used to rewrite the forward transform in
rectangular form:
To start, compare this equation of the complex Fourier transform with the
equation of the real Fourier transform , Eq. 31-1.  At first glance, they appear
to be identical, with only small amount of algebra being required to turn Eq.
31-6 into Eq. 31-1.  However, this is very misleading; the differences between
these two equations are very subtle and easy to overlook, but tremendously
important.   Let's go through the differences in detail.
First, the real Fourier transform converts a real time domain signal, , into x [ n ] two real frequency domain signals,  & .  By using complex Re X [ k ] Im X [ k ] substitution, the frequency domain can be represented by a single complex
array, .  In the complex Fourier transform, both  &  are arrays X [ k ] x [ n ] X [ k ] of complex numbers.  A practical note:  Even though the time domain is
complex, there is nothing that requires us to use the imaginary part.  Suppose
we want to process a real signal, such as a series of voltage measurements
taken over time.  This group of data becomes the real part of the time domain
signal, while the imaginary part is composed of zeros.
Second, the real Fourier transform only deals with positive frequencies.
That is, the frequency domain index, k , only runs from 0 to N /2.  In
comparison, the complex Fourier transform includes both positive and
negative frequencies.  This means k runs from 0 to N -1.  The frequencies
between 0 and N /2 are positive, while the frequencies between N /2 and N -1
are negative.  Remember, the frequency spectrum of a discrete signal is
periodic, making the negative frequencies between N /2 and N -1 the same as
Chapter 31- The Complex Fourier Transform 571
between - N /2 and 0.  The samples at 0 and N /2 straddle the line between
positive and negative.  If you need to refresh your memory on this, look
back at Chapters 10 and 12.
Third, in the real Fourier transform with substitution, a j was added to the sine
wave terms, allowing the frequency spectrum to be represented by complex
numbers.  To convert back to ordinary sine and cosine waves, we can simply
drop the j .  This is the sloppiness that comes when one thing only represents
another thing.  In comparison, the complex DFT, Eq. 31-5, is a formal
mathematical equation with j being an integral part.  In this view, we cannot
arbitrary add or remove a j any more than we can add or remove any other
variable in the equation.
Forth, the real Fourier transform has a scaling factor of two in front, while the
complex Fourier transform does not.  Say we take the real DFT of a cosine
wave with an amplitude of one .  The spectral value corresponding to the cosine
wave is also one .  Now, let's repeat the process using the complex DFT.  In
this case, the cosine wave corresponds to two spectral values, a positive and a
negative frequency.  Both these frequencies have a value of ½.  In other words,
a positive frequency with an amplitude of ½, combines with a negative
frequency with an amplitude of ½, producing a cosine wave with an amplitude
of one . Fifth, the real Fourier transform requires special handling of two frequency
domain samples:  & , but the complex Fourier transform does Re X [ 0 ] Re X [ N / 2 ] not.   Suppose we start with a time domain signal, and take the DFT to find the
frequency domain signal.  To reverse the process, we take the Inverse DFT of
the frequency domain signal, reconstructing the original time domain signal.
However, there is scaling required to make the reconstructed signal be identical
to the original signal.  For the complex Fourier transform, a factor of 1/ N must
be introduced somewhere along the way.  This can be tacked-on to the forward
transform, the inverse transform, or kept as a separate step between the two.
For the real Fourier transform, an additional factor of two is required (2/ N) , as
described above.   However, the real Fourier transform also requires an
additional scaling step:  and  must be divided by two Re X [ 0 ] Re X [ N / 2 ] somewhere along the way.  Put in other words, a scaling factor of 1/ N is used
with these two samples, while 2/ N is used for the remainder of the spectrum.
As previously stated, this awkward step is one of our complaints about the real
Fourier transform.
Why are the real and complex DFTs different in how these two points are
handled?  To answer this, remember that a cosine (or sine) wave in the time
domain becomes split between a positive and a negative frequency in the
complex DFT's spectrum.   However, there are two exceptions to this, the
spectral values at 0 and N /2.  These correspond to zero frequency (DC) and
the Nyquist frequency (one-half the sampling rate).  Since these points
straddle the positive and negative portions of the spectrum, they do not have
a matching point.  Because they are not combined with another value, they
inherently have only one-half the contribution to the time domain as the
other frequencies.
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