变分模态分解_变分模态分解_vmd_

function [u, u_hat, omega] = VMD(signal, alpha, tau, K, DC, init, tol) % Variational Mode Decomposition % Input and Parameters: % --------------------- % signal - the time domain signal (1D) to be decomposed % alpha - the balancing parameter of the data-fidelity constraint（二次惩罚因子:数据保真度约束的平衡参数） % tau - time-step of the dual ascent ( pick 0 for noise-slack )：双递增的时间步长 % K - the number of modes to be recovered(需要分解的模态个数） % DC - true if the first mode is put and kept at DC (0-freq)：如果第一种模式被放置并保持在DC(0频率)，则为真 % init - 0 = all omegas start at 0 % 1 = all omegas start uniformly distributed（均匀分布） % 2 = all omegas initialized randomly（随机初始化） % tol - tolerance of convergence criterion; typically around 1e-6（收敛公差准则;通常大约e-6） % % Output: % ------- % u - the collection of decomposed modes（分解模态的集合） % u_hat - spectra of the modes（模谱） % omega - estimated mode center-frequencies(中心频率的估计模态） % abs是取绝对值函数 % ones是取矩阵函数，行列为1 % sort是升序排序函数 % squeeze是去除矩阵一维行列的函数 % conj函数计算复数的共轭值 % Period and sampling frequency of input signal（输入信号的周期和采样频率） save_T = length(signal); fs = 1/save_T;%分解算法中的采样频率和时间是标准化的，分解信号的采样时间为1s,然后就得到相应的采样频率。采点：length(signal)，频率：1/ length(signal)。 % extend the signal by mirroring（镜像拓展信号） T = save_T; f_mirror(1:T/2) = signal(T/2:-1:1); f_mirror(T/2+1:3*T/2) = signal; f_mirror(3*T/2+1:2*T) = signal(T:-1:T/2+1); f = f_mirror; % Time Domain 0 to T (of mirrored signal) T = length(f); t = (1:T)/T; % Spectral Domain discretization freqs = t-0.5-1/T;%freqs 进行移位是由于进行傅里叶变换时，会有正负对称的频率，分析时一般只有正频率，所以看到的频谱图是没有负频率的。 % Maximum number of iterations (if not converged yet, then it won't anyway) N = 500; % For future generalizations: individual alpha for each mode Alpha = alpha*ones(1,K); % Construct and center f_hat f_hat = fftshift((fft(f))); f_hat_plus = f_hat; f_hat_plus(1:T/2) = 0; % matrix keeping track of every iterant // could be discarded for mem u_hat_plus = zeros(N, length(freqs), K); % Initialization of omega_k omega_plus = zeros(N, K); switch init case 1 for i = 1:K omega_plus(1,i) = (0.5/K)*(i-1); end case 2 omega_plus(1,:) = sort(exp(log(fs) + (log(0.5)-log(fs))*rand(1,K))); otherwise omega_plus(1,:) = 0; end % if DC mode imposed, set its omega to 0 if DC omega_plus(1,1) = 0; end % start with empty dual variables lambda_hat = zeros(N, length(freqs)); % other inits uDiff = tol+eps; % update step n = 1; % loop counter sum_uk = 0; % accumulator % ----------- Main loop for iterative updates while ( uDiff > tol && n < N ) % not converged and below iterations limit % update first mode accumulator k = 1; sum_uk = u_hat_plus(n,:,K) + sum_uk - u_hat_plus(n,:,1); % update spectrum of first mode through Wiener filter of residuals u_hat_plus(n+1,:,k) = (f_hat_plus - sum_uk - lambda_hat(n,:)/2)./(1+Alpha(1,k)*(freqs - omega_plus(n,k)).^2); % update first omega if not held at 0 if ~DC omega_plus(n+1,k) = (freqs(T/2+1:T)*(abs(u_hat_plus(n+1, T/2+1:T, k)).^2)')/sum(abs(u_hat_plus(n+1,T/2+1:T,k)).^2); end % update of any other mode for k=2:K % accumulator sum_uk = u_hat_plus(n+1,:,k-1) + sum_uk - u_hat_plus(n,:,k); % mode spectrum u_hat_plus(n+1,:,k) = (f_hat_plus - sum_uk - lambda_hat(n,:)/2)./(1+Alpha(1,k)*(freqs - omega_plus(n,k)).^2); % center frequencies omega_plus(n+1,k) = (freqs(T/2+1:T)*(abs(u_hat_plus(n+1, T/2+1:T, k)).^2)')/sum(abs(u_hat_plus(n+1,T/2+1:T,k)).^2); end % Dual ascent lambda_hat(n+1,:) = lambda_hat(n,:) + tau*(sum(u_hat_plus(n+1,:,:),3) - f_hat_plus); % loop counter n = n+1; % converged yet? uDiff = eps; for i=1:K uDiff = uDiff + 1/T*(u_hat_plus(n,:,i)-u_hat_plus(n-1,:,i))*conj((u_hat_plus(n,:,i)-u_hat_plus(n-1,:,i)))'; end uDiff = abs(uDiff); end %------ Postprocessing and cleanup % discard empty space if converged early N = min(N,n); omega = omega_plus(1:N,:); % Signal reconstruction u_hat = zeros(T, K); u_hat((T/2+1):T,:) = squeeze(u_hat_plus(N,(T/2+1):T,:)); u_hat((T/2+1):-1:2,:) = squeeze(conj(u_hat_plus(N,(T/2+1):T,:))); u_hat(1,:) = conj(u_hat(end,:)); u = zeros(K,length(t)); for k = 1:K u(k,:)=real(ifft(ifftshift(u_hat(:,k)))); end % remove mirror part u = u(:,T/4+1:3*T/4); % recompute spectrum clear u_hat; for k = 1:K u_hat(:,k)=fftshift(fft(u(k,:)))'; end end