Complex Factor Analysis and Extensions

Mouri Sardarabadi, A. & van der Veen, A-J., 15-Feb-2018, In : IEEE Transactions on Signal Processing. 66, 4, p. 954-967 14 p.

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Original languageEnglish
Pages (from-to)954-967
Number of pages14
JournalIEEE Transactions on Signal Processing
Issue number4
Publication statusPublished - 15-Feb-2018


  • array signal processing, convergence of numerical methods, covariance matrices, gradient methods, least squares approximations, matrix inversion, Newton method, signal denoising, data covariance matrix, general factor analysis decomposition, noise covariance matrix, multiple data covariance matrices, maximum-likelihood based algorithms, complex factor analysis, array signal processing algorithms, eigenvalue decomposition replacement, Gauss-Newton gradient descent method, nonlinear weighted least squares formulation, block diagonal covariance matrix, sparse covariance matrix, noise covariance matrix structure, uncalibrated array, unknown arbitrary diagonal noise covariance, noise covariance parameters, Covariance matrices, Signal processing algorithms, Data models, Computational modeling, Arrays, Approximation algorithms, Mathematical model, Factor analysis, covariance matching, subspace estimation, maximum-likelihood

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