Orthogonal Tensor Decompositions
SIAM Journal on Matrix Analysis and Applications
Resolving Power of Spectral Matrix Filtering: A Discussionon the Links Steering Vectors/Eigenvectors
SSAP '96 Proceedings of the 8th IEEE Signal Processing Workshop on Statistical Signal and Array Processing (SSAP '96)
ESPRIT-based 2-D direction finding with a sparse uniform array ofelectromagnetic vector sensors
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Analysis of a polarized seismic wave model
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Direction-of-arrival estimation via twofold mode-projection
Signal Processing
Sequential high-resolution direction finding from higher order statistics
IEEE Transactions on Signal Processing
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This paper addresses the problem of high-resolution polarized source detection and introduces a new eigenstructure-based algorithm that yields direction of arrival (DOA) and polarization estimates using a vector-sensor (or multicomponent-sensor) array. This method is based on separation of the observation space into signal and noise subspaces using fourth-order tensor decomposition. In geophysics, in particular for reservoir acquisition and monitoring, a set of Nx-multicomponent sensors is laid on the ground with constant distance Δx between them. Such a data acquisition scheme has intrinsically three modes: time, distance, and components. The proposed method needs multilinear algebra in order to preserve data structure and avoid reorganization. The data is thus stored in tridimensional arrays rather than matrices. Higher-order eigenvalue decomposition (HOEVD) for fourth-order tensors is considered to achieve subspaces estimation and to compute the eigenelements. We propose a tensorial version of the MUSIC algorithm for a vector-sensor array allowing a joint estimation of DOA and signal polarization estimation. Performances of the proposed algorithm are evaluated.