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function [yhat, c, e] = lms(x, y, mu, M, c_0) % function [yhat, c, e] = lms(x, y, mu, M, c_0) % FIR Adaptive Filter using LMS Algorithm, % reference to "Statistical and Adaptive Signal Processing" % by Dimitris G. Manolakis, Vinay K. Ingle, and Stephen M. Kogon. McGraw-Hill Higher Education. % x = input sequence % y = desired sequence % mu = step-size % M = filter order % c_0 = initialization of coefficient vector % yhat = filtered sequence % c = FIR filter coefficient vector % e = error signal % P = squared error if nargin ~= 5 error('The input parameters error!') return end if M <= 0 error('The input filter order must be a postive integer!') return end if sum(size(x)>1)>1 || sum(size(y)>1)>1 || sum(size(c_0)>1)>1 error('Input data dimension error!') return end if length(c_0) ~= M error('initial filter order mismatch!') return end x = squeeze(x); y = squeeze(y); Nx = length(x); x = reshape(x,Nx,1); Ny = length(y); y = reshape(y,Ny,1); c_0 = reshape(c_0,M,1); if Ny > Nx y = y(1:Nx); elseif Ny < Nx y = [y;zeros(Nx-Ny,1)]; end yhat = zeros(Nx,1); c = zeros(M,Nx); P = zeros(Nx,1); e = zeros(Nx,1); xm = zeros(M,1); c(:,1) = c_0; for n = 1:Nx if n < M xm(1:n) = flipud(x(1:n)); else xm = flipud(x(n+1-M:n)); end yhat(n) = xm'*c(:,n); e(n) = y(n)-yhat(n); % P(n) = (abs(e(n)))^2; c(:,n+1) = c(:,n)+2*mu*xm*e(n); end return;

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