Lecture 12
description
Transcript of Lecture 12
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NASSP Masters 5003F - Computational Astronomy - 2009
Lecture 12
• Complex numbers – an alternate view
• The Fourier transform
• Convolution, correlation, filtering.
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Complex numbers
REAL IMAGINARY
1iiIRz
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NASSP Masters 5003F - Computational Astronomy - 2009
Complex numbers
REAL IMAGINARY
1iiIRz
NONSENSE!
There IS no √-1.
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NASSP Masters 5003F - Computational Astronomy - 2009
Let’s ‘forget’ about complex numbers for a bit...
...and talk about 2-component vectors instead.
x
y
v
x
y
sin
cosv
y
xv
θ
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NASSP Masters 5003F - Computational Astronomy - 2009
What can we do if we have two of them?
x
y
v1
21
2121 yy
xxvv
v2
We could define something like addition:
There are lots of operations one could define, but only a few of themturn out to be interesting.
vsum
I use a funny symbol to remind us that this is NOTaddition (which is an operationon scalars); it is just analogous to it.:
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NASSP Masters 5003F - Computational Astronomy - 2009
The following operation has interesting properties:
x
y
v1
1221
212121prod yxyx
yyxxvvv
v2
But it isn’t very like scalarmultiplication except whenall ys are zero.
21
2121prod sin
cos
vvv
It’s fairly easy to show that:
vprodθ2
θ1
θprod=θ1+θ2
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NASSP Masters 5003F - Computational Astronomy - 2009
Vectors? These are just complex numbers!
vv
1
0
Note that:
This, plus the angle-summing properties of theproduct, leads to the following typographicalshorthand:
iv expv
Instead of the mysterious
1iwe should just note the simple identity .
0
1
1
0
1
0
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Notation:
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iIRz
izz exp
I
Rz
RIarctan
sincos izz where
These are all just different ways of saying the same thing.
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Some important reals:• Phase
• Power
• Amplitude, magnitude or intensity
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RIarctan =atan2(I,R)
22 IRzzzzP
PzA
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NASSP Masters 5003F - Computational Astronomy - 2009
The lessons to learn:
• Complex numbers are just 2-vectors.• The ‘imaginary’ part is just as real as the
‘real’ part.• Don’t be fooled by the fact that the same
symbols ‘+’ and ‘x’ are used both for scalar addition/multiplication and for what turn out to be vector operations. This is a historical typographical laziness.– Be aware however that the notation I have
used here, although (IMO) more sensible, is not standard.
– So better go with the flow until you get to be a big shot, and stick with the silly x+iy notation.
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The Fourier transform• Analyses a signal into sine and cosines:
• The result is called the spectrum of the signal.NASSP Masters 5003F - Computational Astronomy - 2009
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The Fourier transform
• G in general is complex-valued.• ω is an angular frequency (units: radians per unit t).
• the transform is almost self-inverse:
• But remember, these integrals are not guaranteed to converge. (This is not a problem when we ‘compute’ the FT, as will be seen.)
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titgdtGg expF
tiGdtgG exp1-F
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Typical transform pairs
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point (delta function) fringes.
By the way, ‘the’ reference for the Fourier transform is Bracewell R, “TheFourier Transform and its Applications”, McGraw-Hill
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Typical transform pairs
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‘top hat’ sinc function
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Typical transform pairs
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wider narrower
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Typical transform pairs
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gaussian gaussian
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Typical transform pairs
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Hermitian real
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Practical use of the FT:• Periodic signals hidden in noise
• Processing of pure noise:– Correlation– Convolution– Filtering
• Interferometry
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Periodic signal hidden in noise
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The eye can’t see it… …but the transform can.
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Transforming pure noise
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Uncorrelated noise The transform looks very similar.This sort of noise is called ‘white’. Why?
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Power spectrum• Remember the power P of a complex
number z was defined as
• If we apply this to every complex value of a Fourier spectrum, we get the power spectrum or power spectral density.
• This is both real-valued and positive.• Just as white light contains the same amount
of all frequencies, so does white noise.• (For real data, you have to approximate the
PS by averaging.)NASSP Masters 5003F - Computational Astronomy - 2009
22* IRzzP
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Red, brown or 1/f noise
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It’s fractal – looks the sameat all length scales.
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Nature…?
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No, it is simulated – 1/f2 noise.
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Fourier filtering of noise• Multiply a white spectrum by some band
pass:
• Back-transform:
• The noise is no longer uncorrelated. Now it is correlated noise: ie if the value in one sample is high, this increases the probability that the next sample will also be high.
• I simulated the brown noise in the previous slides via Fourier filtering.
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Another example – bandpass filtering:
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Convolution
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ttgtftdgfth
* =
• It is sort of a smearing/smoothing action.
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A very important result:
• This is often a quick way to do a convolution.
• An example of a convolution met already:– Sliding-window linear filters used in source
detection.
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gfgf FFF
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Correlation
• It is related to convolution:
• Auto-correlation is the correlation of a function by itself.
• NOTE! For f=noise, this integral will not converge..NASSP Masters 5003F - Computational Astronomy - 2009
ttgtftdgftR gf ,
gfgf FFF
ttftftdfftR ff ,
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How to make the autocorrelation converge for a noise signal?
• First recognize that it is often convenient to normalise by dividing by R(0):
• It can be proved that γ(0)=1 and γ(>0)<1.• For ‘sensible’ fs, the following is true:
• A practical calculation estimates equation (1) via some non-infinite value of T. NASSP Masters 5003F - Computational Astronomy - 2009
2tftd
ttftftdt
2
2
2
2
2lim T
T
T
T
T tftd
ttftftdt (1)
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Autocorrelation and power spectrum• From slides 9 and 28, it is easy to show
that the Fourier transform of the autocorrelation of a function is the same as its power spectral density.
• Again, in practice, we normalize the PSD by R(0) and estimate the result over a finite bandwidth.
NASSP Masters 5003F - Computational Astronomy - 2009
22 Ffffff FFFF