LMS ALGORITHM to implement an adaptative filter

I should implement an LMS algorithm for a FIR adaptative filter, to filter the signal ecg where ecg is primary input and is =v+m where v is the desired signal not correlated with r (noise reference input of the filter) m is the noise of the signal ecg correlated with r the LMS algorithm is:

N=10; %orden del filtro

mu=0.4; %parameter

w=zeros(1,N+1); %vector of filter coefficients

e=zeros(1,slen); %vector of error defines as the difference between the desired signal and the input reference signal to compute the filter coefficients to iteratively minimize the error

y=zeros(slen,1);

r=sin(2*pi*60*t); %input reference signal=noise 60 Hz

for n=N+1:slen

    in=r(n-N:n);

    y(n)=w*in';

    e(n)=ecg(n)-y(n);

    w=w+2*mu*e(n)*in;

end

d=filter(w,1,ecg); %desidered output of the adaptative filter

figure(2); plot(t,d);

the problem is that the output is add much more noise to the original ecg filter, instead of reduce it.. Someone can explain me where is/are the error/s..? Thank you very much!

Answer 1

[Wayne King]

What version of MATLAB are you using? Do you have the DSP System Toolbox, see the help adaptfilt.lms