Objectives:
·
To
know how to implement a low pass filter, high pass filter, band pass filter and
band stop filter using IIR filter.
·
To
learn how to implement an inverse filter for low pass filter and band pass
filter.
Equipment and tools:
·
A
computer having code composal studio and Matlab.
·
A
6713dsk toolkit.
·
A
picoscope.
Theory
Adaptive
filters are filters with varying coefficients. These filters are used in
applications where a given system is changing its coefficients in an unknown manner.
A typical system can be either a communication channel whose coefficients are
changing with either temperature or number of users.
In such
cases it is highlydesirable to design the filter to be selflearning so that it
can adapt itself to the situationat hand. The
coefficients of an adaptive filter are adjusted to compensate for changes
ininput signal, output signal, or system parameters. Instead of being rigid, an
adaptivesystem can learn the signal characteristics and track slow changes. An
adaptive filtercan be very useful when there is uncertainty about the
characteristics of a signal orwhen these characteristics change.
Conceptually,
the adaptive scheme is fairly simple. Most of the adaptive scheme scan be
described by the structure shown in Figure 1. This is a basic adaptive filter structure in which
the adaptive filter’s output is
compared with a desired signal to yield an error signal , which is fed back to the adaptive filter.
The error
signal is input to the adaptive algorithm, which adjusts the filter’s
coefficients to satisfy some predetermined criteria or rules. The desired signal
is usually the most difficult one to obtain. One of the first questions that
probably comes to mind is:Why are we trying to generate the desired signal at if
we already know it? Surprisingly, in many applications the desired signal does
exist somewhere in the system or is known a prior.The challenge in applying
adaptive techniques is to figure out where to get the desired signal, what to
make the output , and what to make the error .
Figure 1Basic adaptive filter
structure.
The coefficients of the adaptive filter are adjusted, or optimized,
using an LMS algorithm based on the error signal. Here we discuss only the LMS
searching algorithm with a linear combiner (FIR filter), although there are
several strategies for performing adaptive filtering. The output of the
adaptive filter in Figure
1 is
Applications of adaptive filters
Adaptive
filters have been used for different applications such as:
1.
For
noise cancellation as illustrated inFigure
2. The desired signal is corrupted by uncorrelated
additive noise . The input to the adaptive filter is a noise that is correlated with the noise . The noise could come from the same
source as but modified by the
environment. The adaptive filter’s output is adapted to the noise . When this happens, the error signal approaches the desired signal
. The overall output is this error signal and not the adaptive
filter’s output . If is uncorrelated with , the strategy is to minimize , where is the expected value. The
expected value is generally unknown;therefore, it is usually approximated with
a running average or with the instantaneousfunction itself. Its signal
component,, will be unaffected and onlyits noise component will be minimized.
Experimental procedures
In this experiment an adaptive filter for noise cancellation and
system identification will be designed and implemented.
Adaptive Filter for Sinusoidal Noise Cancellation
This example illustrates the application of the LMS criterion to
cancel an undesirable sinusoidal noise.
A
desired sine wave of 1500 Hz with an additive (undesired) sine wave noise of
312 Hz forms one of two inputs to the adaptive filter structure. A reference
(template) cosine signal, with a frequency of 312Hz, is the input to a
30coefficient adaptive FIR filter. The 312Hz reference cosine signal is
correlated with the 312Hz additive sine noise but not with the 1500Hz desired
sine signal.
For
each time , the output of the adaptive FIR filter is calculated and the 30
weights or coefficients are updated along with the delay samples. The error
signal is the overall desired
output of the adaptive structure. This error signal is the difference between
the desired signal and additive noise (dplusn) and the adaptive filter’s
output, .
To
perform these parts of the experiment follow these steps
1.
In
MATLAB,generate the desired signal, the noise plus the desired signal and the
reference noise according to the following MATLAB commands

1.
In
your code composer studio use the following code to implement an adaptive
filter
#include "DSK6713_AIC23.h" //codecDSK
support file
Uint32 fs= DSK6713_AIC23_FREQ_8KHZ; //set sampling
rate
#include "refnoise.h" //cosine 312 Hz
#include "dplusn.h" //sin(1500) +
sin(312)
#define beta 1E10 //rate of convergence
#define N 30 //# of weights (coefficients)
#define NS 128 //# of output sample points
float w[N]; //buffer weights of adapt filter
float delay[N]; //input buffer to adapt filter
short output; //overall output
short out_type = 1; //output type for slider
interrupt void c_int11() //ISR
{
short i;
static short buffercount=0; //init count of # out
samples
float yn, E; //output filter/"error"
signal
delay[0] = refnoise[buffercount]; //cos(312Hz) input
to adapt FIR
yn = 0; //init output of adapt filter
for (i = 0; i < N; i++) //to calculate out of
adapt FIR
yn += (w[i] * delay[i]); //output of adaptive
filter
E = dplusn[buffercount]  yn; //"error"
signal=(d+n)yn
for (i = N1; i >= 0; i) //to update weights
and delays
{
w[i] = w[i] + beta*E*delay[i]; //update weights
delay[i] = delay[i1]; //update delay samples
}
buffercount++; //increment buffer count
if (buffercount>= NS) //if buffercount=# out
samples
buffercount = 0; //reinit count
if (out_type == 1) //if slider in position 1
output = ((short)E*10); //"error" signal
overall output
else if (out_type == 2) //if slider in position 2
output=dplusn[buffercount]*10;
//desired(1500)+noise(312)
output_sample(output); //overall output result
return; //return from ISR
}
void main()
{
short T=0;
for (T = 0; T < 30; T++)
{
w[T] = 0; //init buffer for weights
delay[T] = 0; //init buffer for delay samples
}
comm_intr(); //init DSK, codec, McBSP
while(1); //infinite loop
}

2.
Write
a slider code to change the parameter out_type from 1 to 2
3.
Build
and execute the project
4.
Plot
the signal that as you see on the screen of the oscilloscope if the out_type
variable is set to 1.
Question 1.
What
signal you are measuring on the oscilloscope screen?
The signal is
that signal we desire pluse the noise.
1.
Plot
the signal that as you see on the screen of the oscilloscope if the out_type
variable is set to 2.
Question 1. What
signal you are measuring on the oscilloscope screen?It
is the sinal we desire to have after it goes throw the adabtve filter and the
noise canceld due to noise cancelation.
1.
Repeat
the experiment by using Gaussian error signal rather than using a sinusoidal
signal
Adaptive FIR filter for noise cancellation using
external inputs
This example extends the previous one to cancel an undesirable
sinusoidal noise using external inputs. The source program shown below allows
two external inputs: a desired signal and a sinusoidal interference. The
program uses the union structure introduced in Chapter 2 with the project
example loop_stereo. A 32bit signal is captured using this structure that
allows an external 16bit input signal through each channel. The 16bit desired
signal is input through the left channel and the undesirable 16bit signal
through the right channel. An adapter with two connectors at one end for each
input signal and one connector at the other end, which connects to the DSK, was
introduced in Chapter 2 with the loop_stereo project and is required to
implement this example. The basic adaptive structure in Figure 2 is applied here along with the LMS algorithm.
#include
"DSK6713_AIC23.h" //codecDSK support file
Uint32 fs=DSK6713_AIC23_FREQ_48KHZ;
//set sampling rate
#define beta 1E13 //rate of
convergence
#define N 30 //# of weights
(coefficients)
#define LEFT 0
//left channel
#define RIGHT 1 //right channel
float w[N]; //weights for adapt
filter
float delay[N];//input buffer to
adapt filter
short output;//overall output
short out_type = 1;//output type
for slider
volatile union{unsigned intuint;
short channel[2];}AIC23_data;
interrupt void c_int11()//ISR
{
short i;
float yn=0, E=0, dplusn=0,
desired=0, noise=0;
AIC23_data.uint =
input_sample();//input 32bit from both channels
dplusn
=(AIC23_data.channel[LEFT]);//input left channel
noise =
(AIC23_data.channel[RIGHT]);//input right channel
delay[0] = noise; //noise as input
to adapt FIR
for (i = 0; i < N; i++) //to
calculate out of adapt FIR
yn += (w[i] * delay[i]); //output
of adaptive filter
E = (dplusn)  yn;
//"error" signal=(d+n)yn
for (i = N1; i >= 0; i) //to
update weights and delays
{
w[i] = w[i] + beta*E*delay[i];
//update weights
delay[i] = delay[i1]; //update
delay samples
}
if(out_type == 1) //if slider in
position 1
output=((short)E); //error signal
as overall output
else if(out_type==2) //if slider
in position 2
output=((short)dplusn); //output
(desired+noise)
output_sample(output); //overall
output result
return;
}
void main()
{
short T=0;
for (T = 0; T < 30; T++)
{
w[T] = 0; //init buffer for
weights
delay[T] = 0; //init buffer for
delay samples
}
comm_intr(); //init DSK, codec,
McBSP
while(1); //infinite loop
}


1.
Run
the program. Verify that the 2kHz noise signal is being canceled gradually.
You can adjust the rate of convergence by changing beta by a factor of 10 in
the program.
2.
Access/load
the slider program adaptnoise_2IN.gel and change the slider position from 1 to
2. Verify the output as the two original sinusoidal signals at 1.5 kHz and at 2
kHz.
3.
Desired:
wideband random noise; undesired: 2 kHz. Input random noise (from a noise
generator, MATLAB.) as the desired wideband signal into the left input channel
and the undesired 2kHz sinusoidal noise signal into the right input channel.
Restart/run the program. Verify that the 2kHz sinusoidal noise signal is being
canceled gradually, with the wideband random noise signal remaining. With the
slider in position 2, observe that both the undesired and desired input signals
are.
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