Resolving power of spectral matrix filtering: a discussion on the links steering vectors/eigenvectors

Thirion, N.; Lacoume, Jean-Louis; Mars, Jérôme

doi:10.1109/ssap.1996.534886

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…After performing efficient average, a decorrelation of waves from themselves and waves from noise is obtained and the spectral matrix is well estimated. Under these conditions, Thirion et al in [24][25][26] have shown that the steering vectors are identifiable to the eigenvectors. In fact steering vectors that account several frequencies (wideband context) can easily show to be asymptotically orthogonal.…”

Section: Estimation Of Signal Subspacementioning

confidence: 99%

“…The upper plot corresponds to the modulus of diagonals of blocks (1) and (4) of Γ s,1 . It enables to determine the frequency band L1 = [ f inf 1 , f sup 1 ] used to estimate the polarization parameters as well as to carry out the estimation of the amplitude ratio α 1 between components X and Z (see (25)). The bottom graph of Figure 12 shows the phase of blocks (2) and (3) diagonals.…”

Section: Synthetic Examplesmentioning

confidence: 99%

“…The bottom graph of Figure 12 shows the phase of blocks (2) and (3) diagonals. It allows to obtain phase shift ϕ 1 between components X and Z (see (25)). In a similar way, it is possible to estimate the polarization parameters of wave 2 by using the principal and secondary diagonals of the spectral matrix Γ s, 2 .…”

Section: Synthetic Examplesmentioning

confidence: 99%

See 2 more Smart Citations

Vector-Sensor Array Processing for Polarization Parameters and DOA Estimation

Paulus

Mars

2010

EURASIP J. Adv. Signal Process.

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This paper presents a method allowing a complete characterization of wave signals received on a vector-sensor array. The proposed technique is based on wavefields separation processing and on estimation of fundamental waves attributes as the state of polarization state and the direction of arrival. Estimation of these attributes is an important step in data processing for a wide range of applications where vector sensor antennas technology is involved such as seismic processing, electromagnetic fields studies, and telecommunications. Compared to the classic techniques, the proposed method is based on computation of multicomponent wideband spectral matrices which enable to take into account all information given by the vector sensor array structures and thus provide a complete characterization of a larger number of sources.

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Section: Estimation Of Signal Subspacementioning

confidence: 99%

Section: Synthetic Examplesmentioning

confidence: 99%

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Vector-Sensor Array Processing for Polarization Parameters and DOA Estimation

Paulus

Mars

2010

EURASIP J. Adv. Signal Process.

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“…In geophysical prospection, because of the impulsive nature of sources, the recorded signals have a strong nonstationary behavior. In order to decorrelate the signals in the interspectral matrix, several smoothing techniques have been proposed [15,16,17,18], mainly based on the spectral and spatial diversity of the signals. The drawback of spatial smoothing is the reduction of effective array-aperture length, resulting in lower resolution and accuracy, while the frequency averaging induces bias in the DOA estimates.…”

Section: Spectral Matrix and Scalar-music Estimatormentioning

confidence: 99%

Vector-Sensor MUSIC for Polarized Seismic Sources Localization

Miron

Bihan

Mars

2005

EURASIP J. Adv. Signal Process.

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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 -multicomponent sensors is laid on the ground with constant distance 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.

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Blind identification methods applied to Electricite de France's civil works and power plants monitoring

d'Urso¹,

Prieur²,

Vincent³

Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics

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Resolving power of spectral matrix filtering: a discussion on the links steering vectors/eigenvectors

Cited by 3 publications

References 8 publications

Vector-Sensor Array Processing for Polarization Parameters and DOA Estimation

Vector-Sensor Array Processing for Polarization Parameters and DOA Estimation

Vector-Sensor MUSIC for Polarized Seismic Sources Localization

Blind identification methods applied to Electricite de France's civil works and power plants monitoring

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