盲源分离JADE算法matlab代码

以下是盲源分离JADE算法的MATLAB代码：

function [S_est, A_est] = jade_bss(X, p)
% JADE algorithm for blind source separation
%
% Inputs:
%   X: M x N matrix containing N samples of M sensors
%   p: number of sources to be estimated
%
% Outputs:
%   S_est: p x N matrix containing N samples of p sources
%   A_est: M x p matrix containing the mixing coefficients

[M, N] = size(X);

% Preprocessing
X_mean = mean(X, 2);
X = X - repmat(X_mean, 1, N);

% Whitening
[V, D, ~] = svd(X*X' / N);
W = V * diag(sqrt(1 ./ diag(D))) * V';
Z = W * X;

% JADE algorithm
[Y, ~] = qr(Z', 0);
B = eye(p);
for i = 1:p*(p-1)/2
    [~, (k, l)] = max(abs(tril(Y' * Y, -1)));
    theta = atan((Y(l, k) / Y(k, k) + eps) / (1 + eps));
    G = eye(p);
    G(k, k) = cos(theta);
    G(l, l) = cos(theta);
    G(l, k) = sin(theta);
    G(k, l) = -sin(theta);
    B = B * G;
    Y = Y * G';
end

A_est = W * B;
S_est = B' * X;
end

其中，输入矩阵 `X` 是一个大小为 `M x N` 的矩阵，表示有 `N` 个样本和 `M` 个传感器。`p` 是需要估计的源数量。输出矩阵 `S_est` 是一个大小为 `p x N` 的矩阵，表示 `N` 个样本的 `p` 个估计源。输出矩阵 `A_est` 是一个大小为 `M x p` 的矩阵，表示 `M` 个传感器和 `p` 个估计源之间的混合系数。