A CANDECOMP/PARAFAC Perspective on Uniqueness of DOA Estimation Using a Vector Sensor Array

We address the uniqueness problem in estimating the directions-of-arrival (DOAs) of multiple narrowband and fully polarized signals impinging on a passive sensor array composed of identical vector sensors. The data recorded on such an array present the so-called "multiple invariances," whi...

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Vydané v:IEEE transactions on signal processing Ročník 59; číslo 7; s. 3475 - 3481
Hlavní autori: Xijing Guo, Miron, S, Brie, D, Shihua Zhu, Xuewen Liao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York, NY IEEE 01.07.2011
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Applied sciences
Arrays
CANDECOMP/PARAFAC uniqueness
Coherence
Computer Science
Data models
Decomposition
Detection, estimation, filtering, equalization, prediction
Direction of arrival estimation
Engineering Sciences
Exact sciences and technology
identifiability
Information, signal and communications theory
Loading
Mathematical analysis
Matrix decomposition
Miscellaneous
Narrowband
polarization
Sensor arrays
Signal and communications theory
Signal and Image Processing
Signal processing
Signal, noise
Studies
Telecommunications and information theory
Tensile stress
Uniqueness
vector sensor array processing
Vectors
Vectors (mathematics)
Passive component
vector sensor array processing
Parameter estimation
Direction-of-arrival estimation
Identifiability
Array signal processing
Signal estimation
Invariance
Narrow band
CANDECOMP/PARAFAC uniqueness
Upper bound
Sufficient condition
PARAFAC decomposition
Signal processing
polarization
Sensor array
Signal detection
identifiability
Vector sensor array processing
ISSN:1053-587X, 1941-0476
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Popis
Shrnutí:We address the uniqueness problem in estimating the directions-of-arrival (DOAs) of multiple narrowband and fully polarized signals impinging on a passive sensor array composed of identical vector sensors. The data recorded on such an array present the so-called "multiple invariances," which can be linked to the CANDECOMP/PARAFAC (CP) model. CP refers to a family of low-rank decompositions of three-way or higher way (mutidimensional) data arrays, where each dimension is termed as a "mode." A sufficient condition is derived for uniqueness of the CP decomposition of a three-way (three mode) array in the particular case where one of the three loading matrices, each associated to one mode, involved in the decomposition has full column rank. Based on this, upper bounds on the maximal number of identifiable DOAs are deduced for the two typical cases, i.e., the general case of uncorrelated or partially correlated sources and the case where the sources are coherent.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2011.2131134