Vector-Sensor MUSIC for Polarized Seismic Sources Localization

Abstract

<p/> <p>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 <it>signal</it> and <it>noise</it> subspaces using fourth-order tensor decomposition. In geophysics, in particular for reservoir acquisition and monitoring, a set of <inline-formula><graphic file="1687-6180-2005-280527-i1.gif"/></inline-formula>-multicomponent sensors is laid on the ground with constant distance <inline-formula><graphic file="1687-6180-2005-280527-i2.gif"/></inline-formula> 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.</p

Vector Sensor Array Processing for Polarized Sources Using a Quadrilinear Representation of the Data Covariance

“…It is compared to MUSIC algorithm for polarized sources, presented under different versions in Ferrara and Parks (1983); Gong et al (2009);Miron et al (2005); Weiss and Friedlander (1993b), to TALS Guo et al (2008) and the Cramér-Rao bound for vector sensor arrays proposed by Nehorai Nehorai and Paldi (1994). A number of K = 2 equal power, uncorrelated sources are considered.…”

“…The use of polarization as a third diversity, in addition to the temporal and spatial diversities, in vector sensor array processing, leading to a trilinear mixture model, was proposed for the first time in Miron et al (2005). Based on this model, a PARAFAC-based algorithm for signal detection, was later introduced in Zhang and Xu (2007).…”

A RANK-(<I>L</I>, <I>L</I>, 1) BCD BASED AOA-POLARIZATION JOINT ESTIMATION ALGORITHM FOR ELECTROMAGNETIC VECTOR SENSOR ARRAY

“…The algorithms of comparison include the classic subspace method MUSIC [9,18] and CPD-based algorithm [19]. …”

Joint estimation of sound source location and boundary impedance with physics-driven cosparse regularization

“…The valuable piece information can be exploited in multiple contexts and applications: tracking of sound sources [1,2,3], robotics [4,5,6], seismic imaging [7,8] and medical imaging [9,10]. In speech and sound enhancement, a source position estimate can be used to perform noise reduction, by means of beamforming techniques [11,12,13,14].…”