ecen3513 signal analysis lecture #4 28 august 2006 n read section 1.5 n problems: 1.5-2a-c, 1.5-4,...
DESCRIPTION
Autocorrelation t1+T t1+T R x (τ) = (1/(T-τ)) x(t)x(t+τ)dt t1 Example R x (1) Take x(t1)*x(t1+1) Take x(t1+ε)*x(t1+1+ ε) x(t1+T-1)*x(t1+T)... Add these all together, then averageTRANSCRIPT
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ECEN3513 Signal AnalysisECEN3513 Signal AnalysisLecture #4 28 August 2006Lecture #4 28 August 2006
Read section 1.5Read section 1.5 Problems: 1.5-2a-c, 1.5-4, & 1.5-5Problems: 1.5-2a-c, 1.5-4, & 1.5-5 Quiz Friday (Chapter 1 and/or Correlation)Quiz Friday (Chapter 1 and/or Correlation)
Lecture 5 assignmentLecture 5 assignment Read 1.6, 1.9, 1.10Read 1.6, 1.9, 1.10 Problems: 1.6-6, 1.9-1, 1.9-3Problems: 1.6-6, 1.9-1, 1.9-3
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Autocorrelation Autocorrelations deal with predictability over time. I.E. Autocorrelations deal with predictability over time. I.E.
given an arbitrary point given an arbitrary point x(t1)x(t1), how predictable is , how predictable is x(t1+tau)x(t1+tau)??
time
Volts
t1
tau
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Autocorrelation t1+Tt1+TRRxx((ττ) = (1/(T-) = (1/(T-ττ))) x(t)x(t+) x(t)x(t+ττ)dt)dt
t1 t1
Example RExample Rxx(1)(1)Take x(t1)*x(t1+1)Take x(t1)*x(t1+1)Take x(t1+Take x(t1+ε)*x(t1+1+ ε)...ε)*x(t1+1+ ε).......x(t1+T-1)*x(t1+T).......x(t1+T-1)*x(t1+T)...Add these all together, then averageAdd these all together, then average
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Autocorrelation
If the average (RIf the average (Rxxxx(tau)) is positive...(tau)) is positive... Then x(t) and x(t+tau) tend to be alikeThen x(t) and x(t+tau) tend to be alike
Both positive or both negativeBoth positive or both negative If the average (RIf the average (Rxxxx(tau)) is negative(tau)) is negative
Then x(t) and x(t+tau) tend to be oppositesThen x(t) and x(t+tau) tend to be oppositesIf one is positive the other tends to be negativeIf one is positive the other tends to be negative
If the average (RIf the average (Rxxxx(tau)) is zero(tau)) is zero There is no predictabilityThere is no predictability
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255 point Noise waveform(Adjacent points are independent)
time
Volts
0
Vdc = 0 v, Normalized Power = 1 watt
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Rxx(0) The sequence x(n)The sequence x(n)
x(1) x(2) x(3) ... x(255)x(1) x(2) x(3) ... x(255) multiply it by the unshifted sequence x(n+0)multiply it by the unshifted sequence x(n+0)
x(1) x(2) x(3) ... x(255)x(1) x(2) x(3) ... x(255) to get the squared sequenceto get the squared sequence
x(1)x(1)22 x(2) x(2)22 x(3) x(3)22 ... x(255) ... x(255)22
Then take the time averageThen take the time average[x(1)[x(1)22 +x(2) +x(2)22 +x(3) +x(3)22 ... +x(255) ... +x(255)22]/255]/255
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Rxx(1) The sequence x(n)The sequence x(n)
x(1) x(2) x(3) ... x(254) x(255)x(1) x(2) x(3) ... x(254) x(255) multiply it by the shifted sequence x(n+1)multiply it by the shifted sequence x(n+1)
x(2) x(3) x(4) ... x(255)x(2) x(3) x(4) ... x(255) to get the sequenceto get the sequence
x(1)x(2) x(2)x(3) x(3)x(4) ... x(254)x(255)x(1)x(2) x(2)x(3) x(3)x(4) ... x(254)x(255) Then take the time averageThen take the time average
[x(1)x(2) +x(2)x(3) +... +x(254)x(255)]/254[x(1)x(2) +x(2)x(3) +... +x(254)x(255)]/254
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Autocorrelation Estimate of White Noise
tau (samples)
Rxx
00
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255 point Noise Waveform(Low Pass Filtered White Noise)
Time
Volts
23 points
0
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Autocorrelation Estimate of Low Pass Filtered White Noise
tau samples
Rxx
0
23
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CorrelationExample
t
x(t)
10
3v
t
y(t+τ)
3-τ2-τ
3v
t
x(t) y(t+τ)
10
3vSo long as τ > 1
area = 0 meaning RXY(τ) = 0
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Correlation
t
x(t)
10
3v
t
y(t+τ)
32
3v
t
x(t) y(t+τ)
10
3vτ = 0
area = 0 meaning RXY(0) = 0
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Correlation
t
x(t)
10
3v
t
y(t+τ)
8/35/3
3v
t
x(t) y(t+τ)
10
3vτ = 1/3
area = 0 meaning RXY(1/3) = 0
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Correlation
t
x(t)
10
3v
t
y(t+τ)
7/34/3
3v
t
x(t) y(t+τ)
10
3vτ = 2/3
area = 0 meaning RXY(2/3) = 0
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Correlation
t
x(t)
10
3v
t
y(t+τ)
6/33/3
3v
t
x(t) y(t+τ)
10
τ = 3/3
area = 0 meaning RXY(1) = 0
9v2
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Correlation
t
x(t)
10
3v
t
y(t+τ)
5/32/3
3v
t
x(t) y(t+τ)
3/30
9v2
τ = 4/3
2/3 area = 3 meaning RXY(4/3) = 3
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Correlation
t
x(t)
10
3v
t
y(t+τ)
4/31/3
3v
t
x(t) y(t+τ)
3/30
9v2
τ = 5/3
1/3 area = 6 meaning RXY(5/3) = 6
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Correlation
t
x(t)
10
3v
t
y(t+τ)
3/30/3
3v
t
x(t) y(t+τ)
3/30
9v2
τ = 6/3Up to this point, the time bounds of x(t)y(t+τ) existed from t = 2-τ to t = 1when 1 < τ < 2 (overlap).
area = 9 meaning RXY(2) = 9
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Correlation
t
x(t)
10
3v
t
y(t+τ)
2/3-1/3
3v
t
x(t) y(t+τ)
2/30
9v2 τ = 7/3When τ > 2 this is no longer the case.
area = 6 meaning RXY(7/3) = 6
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Correlation
t
x(t)
10
3v
t
y(t+τ)
1/3-2/3
t
x(t) y(t+τ)
1/30
9v2
τ = 8/3
3v
area = 3 meaning RXY(8/3) = 3
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Correlation
t
x(t)
10
3v
t
y(t+τ)
0/3-3/3
t
x(t) y(t+τ)
0
9v2
τ = 9/3
3v
area = 0 meaning RXY(τ) = 0; τ > 3
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Correlation Asked to solve for an equation for R(Asked to solve for an equation for R(ττ)?)?
DRAWDRAWSOMESOME
PICTURES!!!PICTURES!!!
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FIR Filter (a.k.a. MA Filter)x(t) x(t-Δ)
Δ
delay
w1 wNw2
Filter Output y(t) = w1x(t) + w2x(t-Δ) + wNx(t-(N-1)Δ)
Δ
delay
Δ
delay
x(t-2Δ)
FIR = Finite Impulse ResponseMA = Moving Average
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