Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Joint Approximate Diagonalization of Positive Definite Hermitian Matrices
SIAM Journal on Matrix Analysis and Applications
Complex-valued ICA based on a pair of generalized covariance matrices
Computational Statistics & Data Analysis
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Fast approximate joint diagonalization incorporating weight matrices
IEEE Transactions on Signal Processing
Nonorthogonal joint diagonalization by combining givens and hyperbolic rotations
IEEE Transactions on Signal Processing
Blind separation of cyclostationary sources using joint block approximate diagonalization
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Simple LU and QR based non-orthogonal matrix joint diagonalization
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Time-Varying Autoregressive (TVAR) Models for Multiple Radar Observations
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Blind separation of mixture of independent sources through aquasi-maximum likelihood approach
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixtures of nonstationary sources
IEEE Transactions on Signal Processing
Compact CramÉr–Rao Bound Expression for Independent Component Analysis
IEEE Transactions on Signal Processing
Quadratic optimization for simultaneous matrix diagonalization
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Blind source separation-semiparametric statistical approach
IEEE Transactions on Signal Processing
One-unit second-order blind identification with reference for short transient signals
Information Sciences: an International Journal
Deflation-based separation of uncorrelated stationary time series
Journal of Multivariate Analysis
Hi-index | 35.68 |
We consider the separation of Gaussian sources exhibiting general, arbitrary (not necessarily stationary) covariance structures. First, assuming a semi-blind scenario, in which the sources' covariance structures are known, we derive the maximum likelihood estimate of the separation matrix, as well as the induced Cramér-Rao lower bound (iCRLB) on the attainable Interference to Source Ratio (ISR). We then extend our results to the fully blind scenario, in which the covariance structures are unknown. We show that (under a scaling convention) the Fisher information matrix in this case is block-diagonal, implying that the same iCRLB (as in the semi-blind scenario) applies in this case as well. Subsequently, we demonstrate that the same "semi-blind" optimal performance can be approached asymptotically in the "fully blind" scenario if the sources are sufficiently ergodic, or if multiple snapshots are available.