Post on 13-Jul-2020
EECS0712 Adaptive Signal Processing
2
Introduction to Adaptive Signal Processing (II)
Assoc. Prof. Dr. Peerapol Yuvapoositanon
Dept. of Electronic Engineering
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Content
• Digital Filters
• Overview of Adaptive Signal Processing Applications
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Digital Filters
• Digital Filters is a digital device adjusting frequency and magnitude
• There are two types of Finite Impulse Response (FIR) and Infinite Impulse Response (IIR)
– FIR has no feedback
– IIR has feedback
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• An FIR filter has no feedback
Finite Impulse Response (FIR)
DelayDelay
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Infinite Impulse Response (IIR)
• An IIR filter has feedback
FeedbackFeedback
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Transfer Function I
• Transfer function= A ratio of z-transform
• Z-Transform
z -Transform
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Transfer Function
• Transfer function
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Use z-Transform to find Frequency Response
• Replace z with ,
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Frequency Response from Poles and Zeros
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Zk= Zeros
Pk= Poles
Magni tude of
response at omega
Example
Example for Frequency Response
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Example for Frequency Response
• Put together
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To Summarise…
The coefficients of a Digital Filter determine the desired
frequency response.
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Overview of Adaptive
Signal Processing
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Why we need adaptive?
• Because environment is always changing.
• System needs to be adaptable.
• In electrical engineering, the environment is systems defined by transfer function.
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Multipath in Wireless Communications
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• Signal from transmitter may reach receiver with multipath signals
Multipath
Channel Equalisation
11 00 11 00
11 00 11..33
..66
Without multipath
With multipath
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Channel
Loss of Info.
Frequency fading channel
• Multipath Channel is also called Frequency fading channel
ff tt
Fourier Transform
ff tt
H(f)=1
H(f)
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Without multipath
With multipath
Fading
Channel Equalization
11 00 11 00
11 00
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Channel
ff
ff
H(z)=1
H(z) 11..33
..66
Basic Equalization II
• If we knew the channel H(z), we put a filter W(z) at the receiver
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Channel Digital
Filter
H(z)
xx 11 00 11 00
11..33
..66
W(z)
H(z) x W(z) = 1
Basic Equalization III
• Equaliser is an inverse channel estimation
Channel Equalizer
H(z) W(z)=1/H(z)
xx
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11 00 11 00
11..33
..66
W(z)= H-1(z)
Basic Equalization II
• Adaptive Equaliser finds H-1(z) automatically
Channel Adaptive
Equalizer
H(z)
xx
++ ee
dd
yy ++
-- xx
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11 00 ..66
11..33
LMS Adaptive Algorithm
• error= d-y New value = Old Value + Step * Error* Input
w(n)=w(n-1) + mu*e(n)*x(n)
For n=1:N
end
e(n)= d(n)-y(n)
AlgorithmAlgorithm
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To Summarise…
The coefficients of Adaptive Equaliser are adapted to the
inverse channel H-1(z).
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LMS Algorithm Block
• บล็อก LMS
Normalization
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Example of Adaptive Equalization
• Least Mean Square (LMS) for Adaptive Equalizer
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Signal+Noise, Signal and Error2
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Trend of Error2
Equalizer Response
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Final Tap weights
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• Anti phase =Cancellation
Basic of Noise Cancellation
FeedbackFeedback ANCANC
NoiseNoise Pilot MicPilot Mic
LoudspeakerLoudspeaker
Quiet zone Quiet zone
nn
nn --nn
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Active Noise Cancellation Headphones
• ANC Headphones
LXLX--18 18 Active Noise Cancelling HeadphonesActive Noise Cancelling Headphones
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Diagram of Active Noise Cancellation Headphones
H(z)H(z)
nn nn
yy
11 22
33
H(z) =Acoustic Transfer Function
Ext.MicExt.Mic
Pilot Mic Quiet Zone
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Speaker
DSP Adaptive Algorithm
ANC
LoudspeakerLoudspeaker
AlgorithmAlgorithm
++
Exterior MicExterior Mic
ee
yy nn
Pilot MicPilot Mic
nn
ANCANC
FIRFIR
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ANC Simulink Model
• Dspanc_win32.mdl
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