JADE算法的matlab代码

以下是 JADE 算法在 MATLAB 中的实现代码：

function [S] = jade(X, n_sources)
% JADE algorithm for blind source separation
% Input:
%   X: mixed signal matrix, shape=(n_samples, n_features)
%   n_sources: number of sources
% Output:
%   S: estimated source matrix, shape=(n_samples, n_sources)

[n_samples, n_features] = size(X);
% centering
X = X - mean(X, 1);
% covariance matrix
C = X' * X / n_samples;
% eigendecomposition
[V, D] = eig(C);
[~, ix] = sort(diag(D), 'descend');
V = V(:, ix);
% whitening matrix
D_half = diag(1./sqrt(diag(D(ix, ix))));
whiteM = V * D_half * V';
X_white = X * whiteM;
% kurtosis
k = 3;
g = sum(X_white .^ k, 1);
dg = sum(k * (X_white .^ (k - 1)) .* abs(X_white), 1);
W = eye(n_features);
for i = 1:n_features
    for j = 1:n_features
        if i ~= j
            W(i, j) = (g(i) * dg(j) - g(j) * dg(i)) / ((g(j) ^ 2 - g(i) ^ 2) * dg(i) + (g(i) ^ 2 - g(j) ^ 2) * dg(j));
        end
    end
end
% JADE iterations
Y = X_white * W;
for t = 1:20
    Y2 = Y .^ 2;
    g = sum(Y2, 1);
    dg = sum(2 * Y .* (1 - Y2 ./ g), 1);
    for i = 1:n_features
        for j = 1:n_features
            if i ~= j
                W(i, j) = (g(i) * dg(j) - g(j) * dg(i)) / ((g(j) ^ 2 - g(i) ^ 2) * dg(i) + (g(i) ^ 2 - g(j) ^ 2) * dg(j));
            end
        end
    end
    Y = Y * W;
end
% estimated sources
S = Y * whiteM';
S = S(:, 1:n_sources);
end

其中，输入参数 X 为混合信号矩阵，n_sources 为原始信号数量。输出估计的原始信号矩阵 S。