applications of adaptive filters

1. Applications of adaptive
linear equalizer

2. contents
1. Overview
2. System identification
3. Linear predictor
4. Inverse modeling
5. Jammer suppression
6. Adaptive notch filter
7. Noise canceller
8. Echo cancellation
8.1 voice echo canceller
8.2 data echo canceller

3. 9. foetal monitoring, cancelling of maternal
ECG during labour
10. Removal of ocular artifacts from electron
encephalogram by adaptive filtering
11.Application of adaptive noise cancelling
filter in AC electrical measurement
12.conclusion
8.3 acoustic echo cancelling
8.4 adaptive feedback cancellation in hearing aid

4. Overview
 The goal of the equalizer is to eliminate the
inter symbol interference(ISI) and the
additive noise as much as possible.
 ISI arises because of the spreading of the
transmitted pulse due to the nature of the
channel.
 Equalization with filter to compensate the
distorted signal.

5.  Adaptive filter or preset filter assumes
that the channel is invariant with time
and its filter coefficients are varying
with time according to the change of
channel.

7.  For linear equalizer

8. Adaptive filters are used in
telecommunication
Radar
sonar
video/audio signal processing
Noise cancellation

9. 2.System identification
 This is used to find the unknown system
response which is set parallel to adaptive filter
and both are excited by the same signal, a white
noise
 Here p(z) is our unknown system and w(z)is
adaptive filter. Both are fed with x(n)
 We compares the outputs d(n) and y(n).. And the
error is to be minimized.

10. • P(1) is the impulse response of the
unknown system
• Prefers white noise as input ,so it will
minimize the error because it can coincide
with the impulse response of the unknown
system.
• Best model for the unknown system is
the system whose impulse response
coincides with the N+1 first sample of the
unknown system impulse response.

11. 3.Linear predictor
 Widely used in speech processing
application such as coding in cellular
telephony, speech enhancement and
speech recognition.
 It estimates the value of signal in
future.
 The desired signal is the forward
version of the adaptive filter input
signal ,when adaptive algorithm LMS
,converges this filter model use as

12.  The output y(n)
 According to LMS algorithm the
coefficients are updated

13. 4.Inverse modeling
 Application in area of channel
equalization.
eg: circuit applied to modem to reduce
the channel distortion result from high
speed of the data transmission over
telephone. Here we use equalizer to
avoid the distortion which is the
inverse of the channel transfer
function

14.  W(z)=1/c(z)
 Delayed version of transmitted data
is the desired response for w(z).

15. 5.Jammer suppresser
 Used for the rejection of narrow band
interference in a direct sequence
spread spectrum receiver.

16.  Using 2 jammer is used to counter act
the effect of finite correlation which
lead to the partial cancellation of the
desired signal
X(n) y(n) Estimation
of y(n)

17. 6.Adaptive notch filter
application
• Broadband signals are corrupted by the narrow band
signal interference.
Why notch
filter?
• Adaptive notch filter tracks the drifting interfering
sinusoidal signal
How it
works?
• Take signal and take the 90 phase shift of the same signal
• The centre frequency of notch filter is same as that of
primary sinusoidal noise ,so noise at this frequency
attenuated

18.  Input signal and the
quadrature component is

19. 7.Noise canceller
Eliminates the background noise
• Used in mobile phones and in radio
communication
Ambient noise and output of micro
phone is compared
• Cancels out the noise for desired signal

20. Desired signal with sampling frequency
8khz and for noise is 11khz
Signals used in the noise canceller system

21. Spectrogram of the signal used in noise canceller

22. a)time waveform of the output signal b)spectrogram of the output
signal

23. 8.Echo cancellation
applications
In air crafts,
cancel the
low frequency
noise inside
the cabin for
passenger
comfort
Active
mufflers for
engines
exhaust pipes
In the active
head phones
to cancel the
ambient
noise by
sending anti
noise

24. 8.1voice echo cancelling
 This is different from data echo cancelling
due to non-stationary nature ,filter needs
large no: of co-efficients, signal bandwidth
etc.
 Does speech detection and de-ionising

25. Echo canceller
monitors the
signal from
speaker b to a
Produce
replica of the
echo in a
Compares the
echo with the
original signal
Energy of the
signal is
minimised
since
coefficient of
filter is adapted

26. 8.2 Data echo cancelling
 Xa(n) is send from A to B by two wire
 ByB(n) from terminal B
 H(z) introduces the error, y’(n)

27.  Y’(n)-xA(n)= close to yB(n)
 Reliable transmission occurred
 No: of coefficient required for adaptive
filter is derived from echo duration.
 No of coefficients N=(2D/v)fs
where D is the length of line.
v is the velocity of the electric signal
over the subscriber line
fs is the sampling frequency

28. 8.3 Acoustic echo cancelling

29. 8.3Adaptive feedback
cancellation In hearing aid
 Acoustic feedback refers to acoustic
coupling of loud speaker and the
microphone

30.  Estimate z(n) of the feedback signal
v(n) and subtract this from
microphone signal .
 Desired signal is preserved at the
input of the forward path.
 Requirement is that the feedback
canceller must be adaptive

31. 9.Foetal monitoring, cancelling of
maternal ECG during labour
why?
MFECG(N)=r(n)+d(n)
AFECG=MFECG-
estimated component
• to monitor foetal heart beat
• Because electrodes placed
on mom’s abdomen is
effected by noise
• R(n):corrupted signal
• D(n):desired signal

33. 10.Removal of ocular artifacts from
electron encephalogram by adaptive
filtering
 An electric signal produced around our
cornea due to the movement of our eye:
EOG
 This appears in EEG as noise.
 Output of EEG is the primary
input
 The two references are
correlated with the noise part
 Output is clean EEG

34. 11. Application of adaptive filter
noise cancelling filter in AC
electrical measurement

35. Cancels
the line
beat
frequency
An ADC
calculates
the phase
difference
of the
signal from
2nd adc
To noise
cancelling
filter

36.  Magnetic field sensor connected to 3
ADCs and 4th ADC is used to
sample the data simultaneously with the 3
axis data
Three Axis linear combiner for interference cancellation

37. 12.conclusion
 Used for estimation of non stationary
signals and systems
 Requires low processing delay
 Distinctive feature of each application
is the way the adaptive filter i/p signal
and the desired signal are chosen.
 Efficiency of the adaptive filter
depends on the used technique of
designed algorithm of adaptation

38. Thank
you