In order to improve the blind separation performance of approximate joint diagonalization of real matrix sets and to avoid trivial solutions,a Jacobi-like joint diagonalization algorithm based on QR decomposition is proposed.Using the numerical stability of QR decomposition,the Jacobi rotation matrix is used to decompose the separation matrix into the product of several elementary triangular matrices and orthogonal matrices.The structure of Jacobi rotation matrix and the related elements of the target matrix transformation are used to obtain the optimal parameters.The high-dimensional minimization problem is iteratively transformed into a series of low-dimensional sub-problems,which enhances the recovery accuracy of the source signal.The algorithm complexity is reduced by solving the simplified Frobenius-norm objective function.The simulation results of mixed electrocardiogram(ECG)signals show that compared with QRJ2D,LUCJD and EGJLUD,the proposed algorithm has certain advantages in separation accuracy and convergence speed.
blind source separation(BSS)non-orthogonal joint diagonalizationQR decompositionJacobi-like algorithmECG signal model
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维普
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