jens
b31f3e7435
git-svn-id: http://moon:8086/svn/matlab/trunk@153 801c6759-fa7c-4059-a304-17956f83a07c
98 lines
2.0 KiB
Matlab
Executable File
98 lines
2.0 KiB
Matlab
Executable File
% pflms2(mu,N,C,P,S,L,x,d,w_start)
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function [ee,w,dd,PX,u]=pflms2(mu,N,C,P,S,L,x,d,w_start)
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Lx = length(x);
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Np = S*L;
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K = Lx/L;
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% FLMS Parameter
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gamma = 0.6; % Vergessensfaktor
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alpha = 1.0 % 0 < alpha < 1
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beta = mu/C
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% Initialisierungen
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ee=zeros(Lx,1); % Fehlervektor
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dd=zeros(Lx,1); % Filterausgang, Schaetzung von d
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wp = zeros(C,P); % Gewichtsvektor im Frequenzbereich
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w = zeros(P*S*L,1); % Gewichtsvektor im Zeitbereich
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PX = zeros(C,1); % Schaetzung des Leistungsdichtespektrums
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WSp = zeros(C,P);
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X = zeros(C,S*P);
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u = zeros(C,P*S);
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ys = zeros(C,1);
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YS = zeros(C,1);
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xzp = zeros(Lx+S*L,1);
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pidTbl = [2:P*S];
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pidTbl(P*S) = 1;
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pStbl = [0:P*S-1];
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pStbl(1) = P*S;
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pjiTbl = [2:P];
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pjiTbl(P) = 1;
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xzp(C-L+1:Lx+C-L) = x;
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pid = 1;
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pji = 1;
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flops(0);
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for k=1:K,
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kL = (k-1)*L;
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X(1:C,pid) = dspfft(xzp(kL+1:kL+C),C);
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pS = pid;
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YS = WSp(1:C,1).*X(1:C,pid) *C;
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for p=2:P,
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for i = 1:S
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pS = pStbl(pS);
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end;
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YS = YS + WSp(1:C,p).*X(1:C,pS) *C;
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end;
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ys = real(dspifft(YS,C));
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dd(kL+1:kL+L) = ys(C-L+1:C);
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ee(kL+1:kL+L) = d(kL+1:kL+L) - ys(C-L+1:C);
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E = dspfft([zeros(C-L,1); ee(kL+1:kL+L)],C);
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PX = abs((1-gamma)*conj(X(1:C,pid)).*X(1:C,pid) *C + gamma*PX);
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% umax = alpha
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u(1:C,pid) = (alpha*beta) ./(PX+beta);
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% Update
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pS = pid;
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for p=1:P,
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WSp(1:C,p) = WSp(1:C,p) + u(1:C,pS) .* conj(X(1:C,pS)) .* E *C;
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for i = 1:S
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pS = pStbl(pS);
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end;
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end;
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% Teuer: Projektion jeden p-ten Teilfilters wp pro k-ter Iteration
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% for p=1:P,
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% wp(1:C,p) = real(dspifft(WSp(1:C,p),C));
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% WSp(1:C,p) = dspfft(wp(1:Np,p),C);
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% end;
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% Billig: Projektion eines Teilfilters alternierend pro k-ter Iteration
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wp(1:C,pji) = real(dspifft(WSp(1:C,pji),C));
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WSp(1:C,pji) = dspfft(wp(1:Np,pji),C);
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pji = pjiTbl(pji);
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pid = pidTbl(pid);
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end;
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flops
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% Return estimated filter weights
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for p=1:P,
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w((p-1)*Np+1:p*Np) = wp(1:Np,p);
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end;
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