An adaptive filter is a filter that self-adjusts its transfer function according to an optimization algorithm driven by an error signal. It has two processes: a filtering process that produces an output in response to input, and an adaptation process that adjusts the filter parameters to changing environments based on the error signal. Adaptive filters are commonly implemented as digital FIR filters and are used for applications like system identification, acoustic echo cancellation, channel equalization, and noise cancellation.

1. ADAPTIVE FILTER
(ALA for Digital Signal Processing (2171003))
Submitted By:
YADAV VIJAY R (140403111014)
CHAUDHARI RAVI L (140403111016)
Guided By:
Prof. M. I. Patel
Department of E & C Engineering
Sankalchand Patel College of Engineering
Visnagar-384315, Dist. Mehsana (N. G.)

2. Adaptive filter
 the signal and/or noise characteristics are often
nonstationary and the statistical parameters vary
with time
 An adaptive filter has an adaptation algorithm, that is
meant to monitor the environment and vary the filter
transfer function accordingly
 based in the actual signals received, attempts to find
the optimum filter design

3. Adaptive filter
 The basic operation now involves two processes :
1. a filtering process, which produces an output signal
in response to a given input signal.
2. an adaptation process, which aims to adjust the filter
parameters (filter transfer function) to the (possibly
time-varying) environment
Often, the (average) square value of the error signal is
used as the optimization criterion

4. Adaptive filter
• Because of complexity of the optimizing algorithms
most adaptive filters are digital filters that perform
digital signal processing
 When processing
analog signals,
the adaptive filter
is then preceded
by A/D and D/A
convertors.

5. Adaptive filter
• The generalization to adaptive IIR filters leads to
stability problems
• It’s common to use
a FIR digital filter
with adjustable
coefficients

6. Applications of Adaptive Filters:
Identification
 Used to provide a linear model of an unknown
plant
 Applications:
 System identification

7. Example:
Acoustic Echo Cancellation

11. Applications are many
Digital Communications
(OFDM , MIMO , CDMA, and
RFID)
Channel Equalisation
Adaptive noise cancellation
Adaptive echo cancellation
System identification
Smart antenna systems
Blind system equalisation
And many, many others

12. New Trends in Adaptive Filtering
 Partial Updating Weights.
 Sub-band adaptive filtering.
 Adaptive Kalman filtering.
 Affine Projection Method.
 Time-Space adaptive processing.
 Non-Linear adaptive filtering:-
Neural Networks.
The Volterra Series Algorithm .
Genetic & Fuzzy.
• Blind Adaptive Filtering.