Blind separation of FHSS signals using PARAFAC analysis and quadrilinear least squares

Xiangqian Liu; Nicholas D. Sidiropoulos; Ananthram Swami

Blind separation of FHSS signals using PARAFAC analysis and quadrilinear least squares

Xiangqian Liu, Nicholas D. Sidiropoulos, Ananthram Swami

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

Abstract

This paper considers the problem of blindly separating multiple frequency-hopped spread-spectrum (FHSS) signals using an antenna array, without knowledge of hopping patterns or directions of arrival (DOAs). We propose to use PARAllel FACtor (PARAFAC) analysis of four-way data generated by capitalizing on both spatial and temporal shift invariance. As a preprocessing step, we may identify a hop-free subset of the data by discarding high-entropy spectral slices from the spectrogram. The analysis step is implemented using Quadrilinear Alternating Least Squares (QALS). We discuss identifiability and performance issues, and compare our algorithm with some earlier algebraic approaches. We also discuss robustness issues when QALS is applied to the raw data without first detecting a hop-free data set.

Original language	English (US)
Title of host publication	Proceedings - IEEE Military Communications Conference MILCOM
Pages	1340-1344
Number of pages	5
Volume	2
State	Published - Dec 1 2001
Event	Milcom 2001: Communications for Network-Centric: Creating the Information Force - McLean, VA, United States Duration: Oct 28 2001 → Oct 31 2001

Other

Other	Milcom 2001: Communications for Network-Centric: Creating the Information Force
Country/Territory	United States
City	McLean, VA
Period	10/28/01 → 10/31/01

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@inproceedings{ce493dc3e8044347b75f90ff0f331fab,

title = "Blind separation of FHSS signals using PARAFAC analysis and quadrilinear least squares",

abstract = "This paper considers the problem of blindly separating multiple frequency-hopped spread-spectrum (FHSS) signals using an antenna array, without knowledge of hopping patterns or directions of arrival (DOAs). We propose to use PARAllel FACtor (PARAFAC) analysis of four-way data generated by capitalizing on both spatial and temporal shift invariance. As a preprocessing step, we may identify a hop-free subset of the data by discarding high-entropy spectral slices from the spectrogram. The analysis step is implemented using Quadrilinear Alternating Least Squares (QALS). We discuss identifiability and performance issues, and compare our algorithm with some earlier algebraic approaches. We also discuss robustness issues when QALS is applied to the raw data without first detecting a hop-free data set.",

author = "Xiangqian Liu and Sidiropoulos, {Nicholas D.} and Ananthram Swami",

year = "2001",

month = dec,

day = "1",

language = "English (US)",

volume = "2",

pages = "1340--1344",

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note = "Milcom 2001: Communications for Network-Centric: Creating the Information Force ; Conference date: 28-10-2001 Through 31-10-2001",

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TY - GEN

T1 - Blind separation of FHSS signals using PARAFAC analysis and quadrilinear least squares

AU - Liu, Xiangqian

AU - Sidiropoulos, Nicholas D.

AU - Swami, Ananthram

PY - 2001/12/1

Y1 - 2001/12/1

N2 - This paper considers the problem of blindly separating multiple frequency-hopped spread-spectrum (FHSS) signals using an antenna array, without knowledge of hopping patterns or directions of arrival (DOAs). We propose to use PARAllel FACtor (PARAFAC) analysis of four-way data generated by capitalizing on both spatial and temporal shift invariance. As a preprocessing step, we may identify a hop-free subset of the data by discarding high-entropy spectral slices from the spectrogram. The analysis step is implemented using Quadrilinear Alternating Least Squares (QALS). We discuss identifiability and performance issues, and compare our algorithm with some earlier algebraic approaches. We also discuss robustness issues when QALS is applied to the raw data without first detecting a hop-free data set.

AB - This paper considers the problem of blindly separating multiple frequency-hopped spread-spectrum (FHSS) signals using an antenna array, without knowledge of hopping patterns or directions of arrival (DOAs). We propose to use PARAllel FACtor (PARAFAC) analysis of four-way data generated by capitalizing on both spatial and temporal shift invariance. As a preprocessing step, we may identify a hop-free subset of the data by discarding high-entropy spectral slices from the spectrogram. The analysis step is implemented using Quadrilinear Alternating Least Squares (QALS). We discuss identifiability and performance issues, and compare our algorithm with some earlier algebraic approaches. We also discuss robustness issues when QALS is applied to the raw data without first detecting a hop-free data set.

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M3 - Conference contribution

AN - SCOPUS:0035728710

VL - 2

SP - 1340

EP - 1344

BT - Proceedings - IEEE Military Communications Conference MILCOM

T2 - Milcom 2001: Communications for Network-Centric: Creating the Information Force

Y2 - 28 October 2001 through 31 October 2001

ER -

Blind separation of FHSS signals using PARAFAC analysis and quadrilinear least squares

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