1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.
Transcript of 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.
![Page 1: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/1.jpg)
1
Fourier Representations of Signals & Linear Time-Invariant Systems
Chapter 3
![Page 2: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/2.jpg)
2
Introduction • In the previous chapter, linearity property was
exploited to develop the convolution sum and convolution integral.
• There, the basic idea of convolution is to break up or decompose a signal into sum of elementary function.
• Then, we find the response of the system to each of those elementary function individually and add the responses to get the overall response.
• In this chapter, we will express a signal as a sum of real or complex sinusoids instead of sum of impulses.
![Page 3: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/3.jpg)
3
• The response of LTI system to sinusoids are also sinusoids of the same frequency but with in general, different amplitude and phase.
![Page 4: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/4.jpg)
4
Complex Sinusoids & Frequency Response of LTI System
• The response of an LTI system to a sinusoidal input leads to a characterization of system behaviour that is termed the ‘frequency response’ of the system.
![Page 5: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/5.jpg)
5
Fourier Representation for Four Signal Classes
• There are 4 distinct Fourier representation, each applicable to a different class of signals.
• These 4 classes are defined by the periodicity properties of a signal and whether it is continuous or discrete.
![Page 6: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/6.jpg)
6
Time property Periodic Nonperiodic
Continuous-time Fourier Series (CTFS)
Fourier Transform (CTFT)
Discrete-time Fourier Series (DTFS)
Fourier Transform (DTFT)
Relationship Between Time Properties of a Signal and the Appropriate Fourier Representations
![Page 7: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/7.jpg)
7
The Continuous-Time Fourier Series(CTFS)
![Page 8: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/8.jpg)
8
Objectives
• To develop methods of expressing periodic signals as linear combination of sinusoids, real or complex.
• To explore the general properties of these ways of expressing signals.
• To apply these methods to find the responses of systems to arbitrary periodic signals.
![Page 9: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/9.jpg)
9
Representing a Signal• The Fourier series represents a signal as a linear
combination of complex sinusoids• The responses of LTI system to sinusoids are also
sinusoids of the same frequency but with, in general, different amplitude and phase.
• Expressing signals in this way leads to frequency domain concept, thinking of signals as function of frequency instead of time.
![Page 10: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/10.jpg)
10
Periodic Excitation and Response
![Page 11: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/11.jpg)
11
Aperiodic Excitation and Response
![Page 12: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/12.jpg)
12
Basic Concept & Development of the Fourier Series
![Page 13: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/13.jpg)
13
Linearity and Superposition
If an excitation can be expressed as a sum of complex sinusoidsthe response can be expressed as the sum of responses to complex sinusoids (same frequency but different multiplyingconstant).
![Page 14: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/14.jpg)
14
Continuous-Time
Fourier Series
Concept
![Page 15: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/15.jpg)
15
Conceptual OverviewThe Fourier series represents a signal as a sum of sinusoids.Consider original signal x(t), which we would like to present as alinear combination of sinusoids as illustrated by the dash line.
![Page 16: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/16.jpg)
16
Conceptual Overview (cont…)The best approximation to the dashed-line signal using a constant + one sinusoid of the same fundamental frequency as the dashed-line signal is the solid line.
+
=
![Page 17: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/17.jpg)
17
Conceptual Overview (cont…)The best approximation to the dashed-line signal using a constant + one sinusoid of the same fundamental frequency as the dashed-line signal + another sinusoid of twice the fundamentalfrequency of the dashed-line signal is the solid line.
![Page 18: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/18.jpg)
18
Conceptual Overview (cont…)The best approximation to the dashed-line signal using a constant + three sinusoids is the solid line. In this case, the third sinusoid has zero amplitude, indicating that sinusoid at that frequency does not help the approximation.
![Page 19: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/19.jpg)
19
Conceptual Overview (cont…)The best approximation to the dashed-line signal using a constant + four sinusoids is the solid line (the forth fundamental frequency is three times fundamental frequency of the dashed-line signal). This is a good approximation which gets better with the addition of more sinusoids at higher integer multiples of the fundamental frequency.
![Page 20: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/20.jpg)
20
Trigonometric Form of CTFS
• In the example above, each of the sinusoids used in the approximation above is of the form cos(2ПkfFt+θ) multiplied by a constant to set its amplitude.
• So we can use trigonometry identity:cos(a+b) = cos(a)cos(b) - sin(a)sin(b)sin(a+b) = sin(a)cos(b) + cos(a)sin(b)
• Therefore, we can reformulate this functional form into:
cos(2ПkfFt+θ)= cos(θ) cos(2ПkfFt) - sin(θ)sin(2ПkfFt)
![Page 21: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/21.jpg)
21
Trigonometric Form of CTFS (cont…)
• The summation of all those sinusoids expressed as cosines and sines are called the continuous-time Fourier Series (CTFS).
• In the CTFS, the higher frequency sines and cosines have frequencies that are integers multiples of fundamental frequencies. The multiple is called the harmonic number, k.
![Page 22: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/22.jpg)
22
• If we have function cos(2ПkfFt) or sin(2ПkfFt)
i) k is harmonic number
ii) kfF is highest frequency.
• If the signal to be represented is x(t), the amplitude of the kth harmonic sine will be designed Xs[k] and the amplitude of the kth harmonic cosine will be designed Xc[k].
• Xs[k] and Xc[k] are called sine and cosine harmonic function respectively.
Component of CTFS
![Page 23: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/23.jpg)
23
Complex Sinusoids form of CTFS
• Every sine and cosine can be replaced by a linear combination of complex sinusoids
cos(2ПkfFt) = (ej2ПkfF
t+ e-j2ПkfF
t)/2
sin(2ПkfFt) = (ej2ПkfF
t - e-j2ПkfF
t)/j2
![Page 24: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/24.jpg)
24
Component of CTFS (cont…)
![Page 25: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/25.jpg)
25
CT Fourier Series Definition
0 0
The Fourier series representation x t of a signal x( )
over a time isF
F
t
t t t T
2x X Fj kf tF
k
t k e
where X[k] is the harmonic function, k is the harmonic
number and fF 1 / TF (pp. 240-242). The harmonic function
can be found from the signal as
0
0
21X x
F
F
t Tj kf t
F t
k t e dtT
The signal and its harmonic function form a
indicated by the notation x X .t k
Fourier series
pair F S
![Page 26: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/26.jpg)
26
The Trigonometric CTFSThe fact that, for a real-valued function x(t)
*X Xk k
also leads to the definition of an alternate form of the CTFS, the so-called trigonometric form.
1
x X 0 X cos 2 X sin 2F c c F s Fk
t k kf t k kf t
0
0
2X x cos 2
Ft T
c FF t
k t kf t dtT
0
0
2X x sin 2
Ft T
s FF t
k t kf t dtT
where
![Page 27: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/27.jpg)
27
The Trigonometric CTFSSince both the complex and trigonometric forms of theCTFS represent a signal, there must be relationships between the harmonic functions. Those relationships are
*
*
X 0 X 0
X 0 0, 1,2,3,
X X X
X X X
c
s
c
s
kk k k
k j k k
*
X 0 X 0
X XX , 1,2,3,
2X X
X X2
c
c s
c s
k j kk k
k j kk k
![Page 28: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/28.jpg)
28
Periodicity of the CTFS
![Page 29: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/29.jpg)
29
The dash line are periodic continuations of the CTFS representation
The illustrations show how various kinds of signals are represented by CTFS over a finite time.
![Page 30: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/30.jpg)
30
The dash line are periodic continuations of the CTFS representation
![Page 31: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/31.jpg)
31
Linearity of the CTFS
These relations hold only if the harmonic functions X of allthe component functions x are based on the samerepresentation time.
![Page 32: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/32.jpg)
32
Magnitude and Phase of X[k]A graph of the magnitude and phase of the harmonic functionas a function of harmonic number is a good way of illustrating it.
![Page 33: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/33.jpg)
33
CTFS of Even and Odd Functions
For an , the complex CTFS harmonic function
X is and the sine harmonic function X is
zero.
For an , the complex CTFS harmonic function
X is and
sk k
k
even function
purely real
odd function
purely imaginary
the cosine harmonic function
X is zero.c k
![Page 34: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/34.jpg)
34
Numerical Computation of the CTFSHow could we find the CTFS of this signal which has noknown functional description?
Numerically.
21X x F
F
j kf t
TF
k t e dtT
Unknown
![Page 35: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/35.jpg)
35
Numerical Computation of the CTFS
We don’t know the function x(t), but if we set of NF samples over one period starting at t=0, the time between the samples is Ts TF/NF, and we can approximate the integral by the sum of several integrals, each covering a time of lenght Ts.
![Page 36: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/36.jpg)
36
Numerical Computation of the CTFS (cont…)
11
2
0
1X x
sF
F s
s
n TNj kf nT
snF nT
k nT e dtT
Samples from x(t)
![Page 37: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/37.jpg)
37
Numerical Computation of the CTFS (cont…)
X 1/ x , F s Fk N nT k N DFT
where
1
2 /
0
x xF
F
Nj nk N
s sn
nT nT e
D F T
![Page 38: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/38.jpg)
38
Convergence of the CTFS
• To examine how the CTFS summation approaches the signal it represents as the number of terms used in the sum approaches infinity.
• We do this by examining the partial sum.
02x XN
j kf tN
k N
t k e
![Page 39: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/39.jpg)
39
Convergence of the CTFS (cont…)
For continuous signals, convergence is exact at every point.
A Continuous Signal
Partial CTFS Sums
02x XN
j kf tN
k N
t k e
![Page 40: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/40.jpg)
40
Convergence of the CTFS (cont…)
For discontinuous signals, convergence is exact at every point of continuity.
Discontinuous Signal
Partial CTFS Sums
![Page 41: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/41.jpg)
41
Convergence of the CTFS (cont…)
At points of discontinuitythe Fourier seriesrepresentation convergesto the mid-point of thediscontinuity.
![Page 42: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/42.jpg)
42
CTFS Properties
Linearity
x y X Yt t k k F S
0
0
Let a signal x( ) have a fundamental period and let a
signal y( ) have a fundamental period . Let the CTFS
harmonic functions, each using a common period as the
representation time, be X[ ] a
x
y
F
t T
t T
T
k nd Y[ ]. Then the following
properties apply.
k
![Page 43: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/43.jpg)
43
CTFS Properties
Time Shifting 0 02
0x Xj kf tt t e k F S
0 00x Xjk tt t e k F S
![Page 44: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/44.jpg)
44
CTFS Properties (cont…)
Frequency Shifting (Harmonic Number
Shifting)
0 020x Xj k f te t k k F S
0 00x Xjk te t k k F S
A shift in frequency (harmonic number) corresponds to multiplication of the time function by a complex exponential.
Time Reversal x Xt k F S
![Page 45: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/45.jpg)
45
CTFS Properties (cont…)Time Scaling
Let z x , 0t at a
0 0Case 1. / for zF x zT T a T t
Z Xk k
0Case 2. for zF xT T t
If a is an integer,
X / , / an integerZ
0 , otherwise
k a k ak
![Page 46: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/46.jpg)
46
CTFS Properties (cont…)Time Scaling (continued)
X / , / an integerZ
0 , otherwise
k a k ak
![Page 47: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/47.jpg)
47
CTFS Properties (cont…)
Change of Representation Time
0With , x XF xT T t k F S
X / , / an integerX
0 , otherwisem
k m k mk
(m is any positive integer)
0With , x XF x mT mT t k F S
![Page 48: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/48.jpg)
48
CTFS Properties (cont…)Change of Representation Time (cont..)
![Page 49: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/49.jpg)
49
CTFS Properties (cont…)
Time Differentiation
0
0
x 2 X
x X
dt j kf k
dtd
t jk kdt
F S
F S
![Page 50: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/50.jpg)
50
Time Integration
Case 1. X 0 0
0
Xx
2
t kd
j kf
F S
0
Xx
t kd
j k
F S
Case 2. X 0 0
xt
d is not periodic
CTFS Properties (cont…)
![Page 51: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/51.jpg)
51
CTFS Properties (cont…)Multiplication-Convolution Duality
x y X Yt t k k F S
(The harmonic functions, X[ ] and Y[ ], must be based
on the same representation period .)F
k k
T
0x y X Yt t T k kF S#
0
x y x yT
t t t d #
x t y t xap t y t where xap t is any single period of x t
The symbol indicates .
Periodic convolution is defined mathematically by
periodic convolution#
![Page 52: 1 Fourier Representations of Signals & Linear Time-Invariant Systems Chapter 3.](https://reader035.fdocuments.us/reader035/viewer/2022062217/56649efb5503460f94c0dcf3/html5/thumbnails/52.jpg)
52
CTFS Properties (cont…)
Conjugation
* *x Xt k F S
Parseval’s Theorem
0
2 2
0
1x X
Tk
t dt kT
The average power of a periodic signal is the sum of theaverage powers in its harmonic components.