CANDECOMP/PARAFAC decomposition based multi-dimensional nonuniform harmonic retrieval

Two CANDECOMP/PARAFAC decomposition based multi-dimensional nonuniform harmonic retrieval algorithms are derived, which are referred to as search efficient Tensor-MUSIC (SE-T-MUSIC) and generalized Tensor-ESPRIT (G-T-ESPRIT). Comparing with the conventional Tensor-MUSIC algorithm, SE-T-MUSIC reduces...

Full description

Saved in:
Bibliographic Details
Main Authors: Fuxi Wen, Wei Liu
Format: Conference Proceeding
Language:English
Subjects:
Computational complexity
CP decomposition
Estimation
Frequency estimation
Generalized Tensor-ESPRIT
Geometry
Harmonic analysis
multi-dimensional nonuniform harmonic retrieval (HR)
Multiple signal classification
rank reduction
Searching efficient Tensor-MUSIC
Tensile stress
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Two CANDECOMP/PARAFAC decomposition based multi-dimensional nonuniform harmonic retrieval algorithms are derived, which are referred to as search efficient Tensor-MUSIC (SE-T-MUSIC) and generalized Tensor-ESPRIT (G-T-ESPRIT). Comparing with the conventional Tensor-MUSIC algorithm, SE-T-MUSIC reduces the computational complexity significantly in terms of the number of searching grids. On the other hand, G-T-ESPRIT is a search-free polynomial rooting based algorithm. It is a R-dimensional generalization of the conventional generalized ESPRIT approach and multidimensional optimization is not required. Furthermore, a CP decomposition based combinatorial search method is proposed to associate the estimated frequencies over R dimensions.
ISSN:2165-3577
DOI:10.1109/ICDSP.2016.7868539