LMS波束形成代碼

LMS波束形成代碼（matlab）LMS算法的仿真程序：%lms 算法clear allclose allhold off%系統(tǒng)信道權(quán)數(shù)sysorder = 5 ;%抽頭數(shù)N=1000;%總采樣次數(shù)inp = randn(N,1);%產(chǎn)生高斯隨機系列n = randn(N,1);b,a = butter(2,0.25);Gz = tf(b,a,-1);%逆變換函數(shù)h= 0.0976;0.2873;0.3360;0.2210;0.0964;%信道特性向量y = lsim(Gz,inp);%加入噪聲n = n * std(y)/(10*std(n);%噪聲信號d = y + n;%期望輸出信號totallength=size(d,1);%步長N=60 ; %60節(jié)點作為訓(xùn)練序列%算法的開始w = zeros ( sysorder , 1 ) ;%初始化for n = sysorder : Nu = inp(n:-1:n-sysorder+1) ;% u的矩陣y(n)= w * u;%系統(tǒng)輸出e(n) = d(n) - y(n) ;%誤差if n 20mu=0.32;elsemu=0.15;endw = w + mu * u * e(n) ;%迭代方程end%檢驗結(jié)果for n = N+1 : totallengthu = inp(n:-1:n-sysorder+1) ;y(n) = w * u ;e(n) = d(n) - y(n) ;%誤差endhold onplot(d)plot(y,r);title(系統(tǒng)輸出) ;xlabel(樣本)ylabel(實際輸出)figuresemilogy(abs(e) ;% e的絕對值坐標(biāo)title(誤差曲線) ;xlabel(樣本)ylabel(誤差矢量)figure%作圖plot(h, k+)hold onplot(w, r*)legend(實際權(quán)矢量,估計權(quán)矢量)title(比較實際和估計權(quán)矢量) ;axis(0 6 0.05 0.35)2.NLMS算法的仿真程序：%lms 算法clear allclose allhold off%系統(tǒng)信道權(quán)數(shù)sysorder = 5 ;%抽頭數(shù)N=1000;%總采樣次數(shù)inp = randn(N,1);%產(chǎn)生高斯隨機系列n = randn(N,1);b,a = butter(2,0.25);Gz = tf(b,a,-1);%逆變換函數(shù)h= 0.0976;0.2873;0.3360;0.2210;0.0964;%信道特性向量y = lsim(Gz,inp);%加入噪聲n = n * std(y)/(10*std(n);%噪聲信號d = y + n;%期望輸出信號totallength=size(d,1);%步長N=60 ; %60節(jié)點作為訓(xùn)練序列%算法的開始w = zeros ( sysorder , 1 ) ;%初始化for n = sysorder : Nu = inp(n:-1:n-sysorder+1) ;% u的矩陣y(n)= w * u;%系統(tǒng)輸出r(n)=u*u;%自相關(guān)矩陣e(n) = d(n) - y(n) ;%誤差fai=.0001;%修正參數(shù)，為防止u*u過小導(dǎo)致步長值太大而設(shè)置的if n 20mu=0.32;elsemu=0.15;endw = w + mu * u * e(n)/(r(n)+fai) ;%迭代方程end%檢驗結(jié)果for n = N+1 : totallengthu = inp(n:-1:n-sysorder+1) ;y(n) = w * u ;e(n) = d(n) - y(n) ;%誤差endhold onplot(d)plot(y,r);title(系統(tǒng)輸出) ;xlabel(樣本)ylabel(實際輸出)figuresemilogy(abs(e) ;% e的絕對值坐標(biāo)title(誤差曲線) ;xlabel(樣本)ylabel(誤差矢量)figure%作圖plot(h, k+)hold onplot(w, r*)legend(實際權(quán)矢量,估計權(quán)矢量)title(比較實際和估計權(quán)矢量) ;axis(0 6 0.05 0.35)3.RLS算法的仿真程序：% RLS算法randn(seed, 0) ;rand(seed, 0) ;NoOfData = 8000 ; % Set no of data points used for trainingOrder = 32 ; % 自適應(yīng)濾波權(quán)數(shù)Lambda = 0.98 ; % 遺忘因子Delta = 0.001 ; % 相關(guān)矩陣R的初始化x = randn(NoOfData, 1) ;%高斯隨機系列h = rand(Order, 1) ; % 系統(tǒng)隨機抽樣d = filter(h, 1, x) ; % 期望輸出% RLS算法的初始化P = Delta * eye ( Order, Order ) ;%相關(guān)矩陣w = zeros ( Order, 1 ) ;%濾波系數(shù)矢量的初始化% RLS Adaptationfor n = Order : NoOfData ;u = x(n:-1:n-Order+1) ;%延時函數(shù)pi_ = u * P ;%互相關(guān)函數(shù)k = Lambda + pi_ * u ;K = pi_/k;%增益矢量e(n) = d(n) - w * u ;%誤差函數(shù)w = w + K * e(n) ;%遞歸公式PPrime = K * pi_ ;P = ( P - PPrime ) / Lambda ;%誤差相關(guān)矩陣w_err(n) = norm(h - w) ;%真實估計誤差end ;% 作圖表示結(jié)果figure ;plot(20*log10(abs(e) ;%| e |的誤差曲線title(學(xué)習(xí)曲線) ;xlabel(迭代次數(shù)) ;ylabel(輸出誤差估計) ;figure ;semilogy(w_err) ;%作實際估計誤差圖title(矢量估計誤差) ;xlabel(迭代次數(shù)) ;ylabel(誤差權(quán)矢量) ;4.自適應(yīng)均衡器的仿真程序：% Illustration of the conventional RLS algorithmclose all;W=2.9;Nexp=10;N=2000;Nmc=1; % Number of ensemble realizationsM=11;%抽頭系數(shù)lambda=0.99;%遺忘因子varv=0.001;%噪聲方差h=zeros(3,1);%h的初始化er=zeros(N,Nmc);%er的初始化h(1)=0.5*(1+cos(2*pi*(1-2)/W);h(2)=0.5*(1+cos(2*pi*(2-2)/W);h(3)=0.5*(1+cos(2*pi*(3-2)/W);% 學(xué)習(xí)曲線hc=0 h(1) h(2) h(3);n0=7;t=(1:N);for i=1:Nmcy=sign(rand(N,1)-0.5);%輸入信號v=sqrt(varv)*randn(N,1);%噪聲信號x=filter(hc,1,y)+v;%信號混合x=zeros(M-1,1);x;%x矩陣yd=zeros(N+M-1,1); %延遲信號初始化e=yd;yd(n0+M-1:N+M-1)=y(1:N-n0+1);% CRLS 算法% Initializationlambda=0.98;P=(10-3)*eye(M,M);c=zeros(M,1);g=c;glambda=g;% 迭代范圍for n=M:M+N-1xn=flipud(x(n-M+1:n);glambda=P*xn;alphal=lambda+conj(glambda)*xn;g=glambda/lambda;a(n)=1-conj(g)*xn;P=(P-g*conj(glambda)/lambda;P=(P+P)/2;e(n)=yd(n)-conj(c)*xn;c=c+g*conj(e(n);endendeplot=e(M:M+N-1).2;subplot(2,1,1), plot(t,abs(eplot)ylabel(|e(n)|2);xlabel(n);subplot(2,1,2), plot(t,a(M:M+N-1);ylabel(alpha(n);xlabel(n);5.自適應(yīng)陷波器的仿真程序：N=400; %總采樣長度t=0:N-1; %時間的變化范圍s=sin(2*pi*t/20); %輸入信號A=0.5; %干擾信號的幅值fai=pi/3;%干擾信號的相移n=A*cos(2*pi*t/10+fai);%干擾信號x=s+n;%信號混合subplot(2,2,1);%作第一子圖plot(t,s);subplot(2,2,2); %作第二子圖plot(t,x);x1=cos(2*pi*t/10);x2=sin(2*pi*t/10);%初始化w1=0.1;w2=