ATAPESmethod.rar_APES_high_location estimation_spectral peak

clear all clc N=96; % length of sequence M=48; % length of filter L=N-M+1; % number of snap shots Fs = 1; % Sampling frequency Ts = 1/Fs; % Sample time Lx = 1056; % Length of signal t = (0:Lx-1)*Ts; % Time vector w = 0; % frequency that filter passes the xn without distortion A = [1 1 1 0.2 1 0.1 0.1]; % Sinusoid amplitudes f =[0.90;0.102;0.240;0.350;0.370;0.390;0.415]; % Sinusoid frequencies signal=A*sin(2*pi*f*t); % signal noise= 0.1.*randn(size(t)); % noise data=signal+noise; % data=signal+noise xn=[]; y=[]; Yw=[]; for n=0:ceil(Lx/N)-1 % product data sequence for m=1:N xn(n+1,m)=data(1,n*N+m); end end for n=0:ceil(Lx/N)-1 % product y(l) , l=1,...,L for m=1:L y(n*L+m,:)=xn(n+1,m:m+M-1); end end for n=0:ceil(Lx/N)-1 for m=1:L Yw(n+1,:)=Yw+y(n*L+m,:).*exp(-i*w*m)./L; end end % NFFT = 2^nextpow2(L); % Next power of 2 from length of y % X = fft(xn,NFFT)/L; % F = Fs/2*linspace(0,1,NFFT/2+1); % plot(F,2*abs(X(1:NFFT/2+1))) % R=cov(xn); % sample covariance % sigma2=var(n); % variance of noise % T=(1+sigma2); % tuning factor % R_tuning=T*R; % tuning sample covariance % Q_tuning=T*R-X*conj(X'); % w=2*pi*100; % frequency without distortion % a=[]; % for j=1:M % a=[a,exp(i*w*(j-1))]; % end % h_ATAPES=(Q_tuning^(-1)*a)/(conj(a')*Q_tuning^(-1)*a);