Blind PARAFAC signal detection for polarization sensitive array
EURASIP Journal on Applied Signal Processing
4D near-field source localization using cumulant
EURASIP Journal on Applied Signal Processing
Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
Blind Joint Symbol Detection and DOA Estimation for OFDM System with Antenna Array
Wireless Personal Communications: An International Journal
A novel semiblind signal extraction approach for the removal of eye-blink artifact from EEGs
EURASIP Journal on Advances in Signal Processing
Blind multiuser detection for MC-CDMA with antenna array
Computers and Electrical Engineering
Novel blind carrier frequency offset estimation for OFDM system with multiple antennas
IEEE Transactions on Wireless Communications
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Parallel factor (PARAFAC) analysis is an extension of low-rank matrix decomposition to higher way arrays, also referred to as tensors. It decomposes a given array in a sum of multilinear terms, analogous to the familiar bilinear vector outer products that appear in matrix decomposition. PARAFAC analysis generalizes and unifies common array processing models, like joint diagonalization and ESPRIT; it has found numerous applications from blind multiuser detection and multidimensional harmonic retrieval, to clustering and nuclear magnetic resonance. The prevailing fitting algorithm in all these applications is based on (alternating) least squares, which is optimal for Gaussian noise. In many cases, however, measurement errors are far from being Gaussian. In this paper, we develop two iterative algorithms for the least absolute error fitting of general multilinear models. The first is based on efficient interior point methods for linear programming, employed in an alternating fashion. The second is based on a weighted median filtering iteration, which is particularly appealing from a simplicity viewpoint. Both are guaranteed to converge in terms of absolute error. Performance is illustrated by means of simulations, and compared to the pertinent Crame´r-Rao bounds (CRBs).