Coupled canonical polyadic decompositions and multiple shift invariance in array processing
Mikael Sørensen, Ignat Domanov, Lieven De Lathauwer
Tensor decompositions such as the canonical polyadic decomposition (CPD) or the block term decomposition (BTD) are basic tools for blind signal separation. Most of the literature concerns instantaneous mixtures/memoryless channels. In this paper, we focus on convolutive extensions. More precisely, we present a connection between convolutive CPD/BTD models and coupled but instantaneous CPD/BTD. We derive a new identifiability condition dedicated to convolutive low-rank factorization problems. We explain that under this condition, the convolutive extension of CPD/BTD can be computed by means of an algebraic method, guaranteeing perfect source separation in the noiseless case. In the inexact case, the algorithm can be used as a cheap initialization for an optimization-based method. We explain that, in contrast to the memoryless case, convolutive signal separation is in certain cases possible despite only two-way diversities (e.g., space × time).
This package provides the algorithms, experiment files and auxiliary files for the multiple shift invariance array processing paper.
M. Sørensen, I. Domanov, and L. De Lathauwer, "Coupled Canonical Polyadic Decompositions and Multiple Shift Invariance in Array Processing," IEEE Transactions on Signal Processing, vol. 66, no. 14, pp. 3665-3680, July 2018.
This repository can be cited as:
S. Hendrikx, M. Boussé, N. Vervliet, M. Vandecappelle, R. Kenis, and L. De Lathauwer, Tensorlab⁺, Available online, Version of Dec 2022 downloaded from https://www.tensorlabplus.net.