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Convolution, Correlation, & Fourier Transforms

Page 1
Convolution, Correlation, & Fourier Transforms
James R. Graham 11/25/2009

Page 2
Introduction
• A large class of signal processing techniques fall under the category of Fourier transform methods
– These methods fall into two broad categories
• Efficient method for accomplishing common data manipulations • Problems related to the Fourier transform or the power spectrum

Page 3
Time & Frequency Domains
• A physical process can be described in two ways
– In the time domain, by h as a function of time t, that is h(t), -�� < t < �� – In the frequency domain, by H that gives its amplitude and phase as a function of frequency f, that is H(f), with -�� < f < ��
• In general h and H are complex numbers
• It is useful to think of h(t) and H(f) as two different representations of the same function
– One goes back and forth between these two representations by Fourier transforms

Page 4
Fourier Transforms
• If t is measured in seconds, then f is in cycles per second or Hz • Other units
– E.g, if h=h(x) and x is in meters, then H is a function of spatial frequency measured in cycles per meter
H(f )= h(t)e
−2��ift
dt
−�� ��
��
h(t)= H(f )e
2��ift
df
−�� ��
��

Page 5
Fourier Transforms
• The Fourier transform is a linear operator
– The transform of the sum of two functions is the sum of the transforms
h
12 = h 1 + h 2
H
12
( f )= h
12
e
−2��ift
dt
−�� ��
��
= h
1 + h 2
( )e
−2��ift
dt
−�� ��
��
= h
1
e
−2��ift
dt
−�� ��
��
+ h
2
e
−2��ift
dt
−�� ��
��
= H
1 + H 2

Page 6
Fourier Transforms
h(t) may have some special properties
– Real, imaginary – Even: h(t) = h(-t) – Odd: h(t) = -h(-t)
• In the frequency domain these symmetries lead to relations between H(f) and H(-f)

Page 7
FT Symmetries
H(f) real & odd h(t) imaginary & odd H(f) imaginary & even h(t) imaginary & even H(f) imaginary & odd h(t) real & odd H(f) real & even h(t) real & even H(-f) = - H(f) (odd) h(t) odd H(-f) = H(f) (even) h(t) even H(-f) = -[ H(f) ]* h(t) imaginary H(-f) = [ H(f) ]* h(t) real Then�� If��

Page 8
Elementary Properties of FT
h(t)↔ H(f ) Fourier Pair h(at)↔ 1 a H(f /a) Time scaling h(t t
0
)↔H(f )e
−2��ift
0
Time shifting

Page 9
Convolution
• With two functions h(t) and g(t), and their corresponding Fourier transforms H(f) and G(f), we can form two special combinations
– The convolution, denoted f = g * h, defined by
f t( )= gh �� g(��)h(t
−�� ��
��
��)d��

Page 10
Convolution
g*h is a function of time, and g*h = h*g
– The convolution is one member of a transform pair
• The Fourier transform of the convolution is the product of the two Fourier transforms!
– This is the Convolution Theorem
gh G( f )H( f )

Page 11
Correlation
• The correlation of g and h • The correlation is a function of t, which is known as the lag
– The correlation lies in the time domain
Corr(g,h) �� g(�� + t)h(��
−�� ��
��
)d��

Page 12
Correlation
• The correlation is one member of the transform pair
– More generally, the RHS of the pair is G(f)H(-f) – Usually g & h are real, so H(-f) = H*(f)
• Multiplying the FT of one function by the complex conjugate of the FT of the other gives the FT of their correlation
– This is the Correlation Theorem
Corr(g,h)↔G( f )H
*
( f )

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Autocorrelation
• The correlation of a function with itself is called its autocorrelation.
– In this case the correlation theorem becomes the transform pair – This is the Wiener-Khinchin Theorem
Corr(g,g)↔G( f )G
*
( f ) = G( f )
2

Page 14
Convolution
• Mathematically the convolution of r(t) and s(t), denoted r*s=s*r • In most applications r and s have quite different meanings
s(t) is typically a signal or data stream, which goes on indefinitely in time – r(t) is a response function, typically a peaked and that falls to zero in both directions from its maximum

Page 15
The Response Function
• The effect of convolution is to smear the signal s(t) in time according to the recipe provided by the response function r(t) • A spike or delta-function of unit area in s which occurs at some time t
0
is
– Smeared into the shape of the response function – Translated from time 0 to time t
0
as r(t - t
0
)

Page 16
Convolution
• The signal s(t) is convolved with a response function r(t)
– Since the response function is broader than some features in the original signal, these are smoothed out in the convolution
s(t) r(t) s*r

Page 17
Fourier Transforms & FFT
• Fourier methods have revolutionized many fields of science & engineering
– Radio astronomy, medical imaging, & seismology
• The wide application of Fourier methods is due to the existence of the fast Fourier transform (FFT) • The FFT permits rapid computation of the discrete Fourier transform • Among the most direct applications of the FFT are to the convolution, correlation & autocorrelation of data

Page 18
The FFT & Convolution
• The convolution of two functions is defined for the continuous case
– The convolution theorem says that the Fourier transform of the convolution of two functions is equal to the product of their individual Fourier transforms
• We want to deal with the discrete case
– How does this work in the context of convolution?
gh G( f )H( f )

Page 19
Discrete Convolution
• In the discrete case s(t) is represented by its sampled values at equal time intervals s
j
• The response function is also a discrete set r
k
r
0
tells what multiple of the input signal in channel j is copied into the output channel jr
1
tells what multiple of input signal j is copied into the output channel j+1r
-1
tells the multiple of input signal j is copied into the output channel j-1 – Repeat for all values of k

Page 20
Discrete Convolution
• Symbolically the discrete convolution is with a response function of finite duration, N, is
sr
( )j
= s
k
r
jk k=−N /2+1 N /2
��
sr
( )j
S
l
R
l

Page 21
Discrete Convolution
• Convolution of discretely sampled functions
– Note the response function for negative times wraps around and is stored at the end of the array r
k
s
j
r
k
(s*r)
j

Page 22
Examples
• Java applet demonstrations
– Continuous convolution
• http://www.jhu.edu/~signals/convolve/
– Discrete convolution
• http://www.jhu.edu/~signals/discreteconv/

Page 23
Suppose that f and g are functions of time f(t) = F(��)e
2��i��t -�� ��
��
d�� and g(t)= G(��)e
2��i��t -�� ��
��
d�� The convolution f*g says f g = g(t')f(t t')
-�� ��
��
dt' = g(t') F(��)e
2��i��(tt ') -�� ��
��
d�� ⎡ ⎣⎢ ⎤ ⎦⎥
-�� ��
��
dt' Swap the order of integration = F(��) g(t')e
−2��i��t '
dt'
-�� ��
��
⎡ ⎣⎢ ⎤ ⎦⎥
-�� ��
��
e
2��i��t
d�� = FT F(��)G(��)
[ ]
Voila!

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