Parallel factor analysis in sensor array processing

This paper links multiple invariance sensor array processing (MI-SAP) to parallel factor (PARAFAC) analysis, which is a tool rooted in psychometrics and chemometrics. PARAFAC is a common name for low-rank decomposition of three- and higher way arrays. This link facilitates the derivation of powerful...

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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 48; no. 8; pp. 2377 - 2388
Main Authors: Sidiropoulos, N.D., Bro, R., Giannakis, G.B.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.08.2000
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Applied sciences
Array signal processing
Arrays
Azimuth
Calibration
Decomposition
Detection, estimation, filtering, equalization, prediction
Direction of arrival estimation
Exact sciences and technology
Fitting
Information, signal and communications theory
Interference suppression
Links
Mathematical models
Psychometric testing
Radar
Sensor arrays
Signal and communications theory
Signal, noise
Stems
Tapping
Telecommunications and information theory
Uniqueness
Wireless communication
Mobile radiocommunication
Monte Carlo method
Identifiability
Factor analysis
Computer simulation
Measurement sensor
Psychometrics
Decomposition
Modeling
Uniqueness theorem
Model matching
Arrival angle
Radar
Sensor array
Chemometrics
Signal to noise ratio
ISSN:1053-587X, 1941-0476
Online Access:Get full text
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Description
Summary:This paper links multiple invariance sensor array processing (MI-SAP) to parallel factor (PARAFAC) analysis, which is a tool rooted in psychometrics and chemometrics. PARAFAC is a common name for low-rank decomposition of three- and higher way arrays. This link facilitates the derivation of powerful identifiability results for MI-SAP, shows that the uniqueness of single- and multiple-invariance ESPRIT stems from uniqueness of low-rank decomposition of three-way arrays, and allows tapping on the available expertise for fitting the PARAFAC model. The results are applicable to both data-domain and subspace MI-SAP formulations. The paper also includes a constructive uniqueness proof for a special PARAFAC model.
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ISSN:1053-587X
1941-0476
DOI:10.1109/78.852018