signals and systems lecture 19: fir filters. 2 today's lecture −system properties: linearity...
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3 Time InvarianceTRANSCRIPT
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Signals and Systems
Lecture 19: FIR Filters
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Today's lecture −System Properties:
Linearity Time-invariance
−How to convolve the signals−LTI Systems characteristics−Cascade LTI Systems
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Time Invariance
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Testing Time-Invariance
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Examples of Time-Invariance−Square Law system
y[n] = {x[n]} 2
−Time Flip systemy[n] = x[- n]
−First Difference system y[n] = x[n] - x[n-1]
−Practice: Prove the system given Exercise 5.9 is not time-invariant
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Linear System
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Testing Linearity
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Practice Problems−y[n] = x[n - 2] – 2 x[n] + x[n + 2]−y[n] = x[n] cos(0.2n)−y[n] = n x[n]
−Are all FIR filters Time-invariant and Linear?
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There are two methods to convolve the signals:
− Graphical Method− Tabular Method
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Convolution− Example
knhkxnyk
5 nununx
41 nununh
0 1 2 3 4123 k
1
knhkx
ny
0 1 2 3 4 5n
6 7
kh kx
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Convolution− Example
knhkxnyk
5 nununx
41 nununh
0 1 2 3 4123 k
1
knhkx
ny
0 1 2 3 4 5n
6 7
1
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Convolution− Example
knhkxnyk
5 nununx
41 nununh
0 1 2 3 4123 k
1
knhkx
ny
0 1 2 3 4 5n
6 7
12
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Convolution− Example
knhkxnyk
5 nununx
41 nununh
0 1 2 3 4123 k
1
knhkx
ny
0 1 2 3 4 5n
6 7
12
3
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Convolution− Example
knhkxnyk
5 nununx
41 nununh
0 1 2 3 4123 k
1
knhkx
ny
0 1 2 3 4 5n
6 7
12
3 3
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Convolution− Example
knhkxnyk
5 nununx
41 nununh
0 1 2 3 4123 k
1
knhkx
ny
0 1 2 3 4 5n
6 7
12
3 3 3
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Convolution− Example
knhkxnyk
5 nununx
41 nununh
0 1 2 3 4123 k
1
knhkx
ny
0 1 2 3 4 5n
6 7
12
3 3 3
5
2
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Convolution− Example
knhkxnyk
5 nununx
41 nununh
0 1 2 3 4123 k
1
knhkx
ny
0 1 2 3 4 5n
6 7
12
3 3 3
5
2
6
1
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Convolution− Example
knhkxnyk
5 nununx
41 nununh
0 1 2 3 4123 k
1
knhkx
ny
0 1 2 3 4 5n
6 7
12
3 3 3
5
2
6
1
7
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Convolution Example
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Convolution and LTI systems−Derivation of the convolution sum
x[n] = ∑ x [l] δ[n - l] for l= any integer
= …... x [-2] δ[n + 2] + x [-1] δ[n + 1] + x [0] δ[n] + x [1] δ[n - 1] + x [2] δ[n - 2] +……. x [0] δ[n] x[0] h[n] x [1] δ[n - 1] x[1] h[n - 1] x [2] δ[n - 2] x[2] h[n - 2] x [l] δ[n - l] x[l] h[n - l]
x[n] = ∑ x [l] δ[n - l] y[n] = ∑ x [l] h[n - l]
l
l l
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Properties of LTI Systems−Convolution with an impulse
x[n] * δ [n - n0] = x[n - n0]
−Commutative Property of convolution x[n] * h [n] = h [n] * x[n]
−Associative Property of convolution(x1[n] * x2 [n] )* x3 [n] = x1 [n] * (x2 [n] * x3
[n])
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Assignment # 4−Problems at the end of chapter 5−P-5.2−P-5.3−P-5.4−P-5.6−P-5.10−P-5.12−Not Decided about Deadline Date−Tell you later