Estimation of DOA for Noncircular Signals via Vandermonde Constrained Parallel Factor Analysis

We provide a complete study on the direction-of-arrival (DOA) estimation of noncircular (NC) signals for uniform linear array (ULA) via Vandermonde constrained parallel factor (PARAFAC) analysis. By exploiting the noncircular property of the signals, we first construct an extended matrix which conta...

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Main Authors: Heyun Lin, Chaowei Yuan, Jianhe Du, Zhongwei Hu
Format: Article
Language:English
Published: Hindawi Limited 2018-01-01
Series:International Journal of Antennas and Propagation
Online Access:http://dx.doi.org/10.1155/2018/4612583
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spelling doaj-d9eb181eb29a494383e99a04cd6208042020-11-24T21:32:20ZengHindawi LimitedInternational Journal of Antennas and Propagation1687-58691687-58772018-01-01201810.1155/2018/46125834612583Estimation of DOA for Noncircular Signals via Vandermonde Constrained Parallel Factor AnalysisHeyun Lin0Chaowei Yuan1Jianhe Du2Zhongwei Hu3School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Information and Engineering, Communication University of China, Beijing 100024, ChinaSchool of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaWe provide a complete study on the direction-of-arrival (DOA) estimation of noncircular (NC) signals for uniform linear array (ULA) via Vandermonde constrained parallel factor (PARAFAC) analysis. By exploiting the noncircular property of the signals, we first construct an extended matrix which contains two times sampling number of the received signal. Then, taking the Vandermonde structure of the array manifold matrix into account, the extended matrix can be turned into a tensor model which admits the Vandermonde constrained PARAFAC decomposition. Based on this tensor model, an efficient linear algebra algorithm is applied to obtain the DOA estimation via utilizing the rotational invariance between two submatrices. Compared with some existing algorithms, the proposed method has a better DOA estimation performance. Meanwhile, the proposed method consistently has a higher estimation accuracy and a much lower computational complexity than the trilinear alternating least square- (TALS-) based PARAFAC algorithm. Finally, numerical examples are conducted to demonstrate the effectiveness of the proposed approach in terms of estimation accuracy and computational complexity.http://dx.doi.org/10.1155/2018/4612583
collection DOAJ
language English
format Article
sources DOAJ
author Heyun Lin
Chaowei Yuan
Jianhe Du
Zhongwei Hu
spellingShingle Heyun Lin
Chaowei Yuan
Jianhe Du
Zhongwei Hu
Estimation of DOA for Noncircular Signals via Vandermonde Constrained Parallel Factor Analysis
International Journal of Antennas and Propagation
author_facet Heyun Lin
Chaowei Yuan
Jianhe Du
Zhongwei Hu
author_sort Heyun Lin
title Estimation of DOA for Noncircular Signals via Vandermonde Constrained Parallel Factor Analysis
title_short Estimation of DOA for Noncircular Signals via Vandermonde Constrained Parallel Factor Analysis
title_full Estimation of DOA for Noncircular Signals via Vandermonde Constrained Parallel Factor Analysis
title_fullStr Estimation of DOA for Noncircular Signals via Vandermonde Constrained Parallel Factor Analysis
title_full_unstemmed Estimation of DOA for Noncircular Signals via Vandermonde Constrained Parallel Factor Analysis
title_sort estimation of doa for noncircular signals via vandermonde constrained parallel factor analysis
publisher Hindawi Limited
series International Journal of Antennas and Propagation
issn 1687-5869
1687-5877
publishDate 2018-01-01
description We provide a complete study on the direction-of-arrival (DOA) estimation of noncircular (NC) signals for uniform linear array (ULA) via Vandermonde constrained parallel factor (PARAFAC) analysis. By exploiting the noncircular property of the signals, we first construct an extended matrix which contains two times sampling number of the received signal. Then, taking the Vandermonde structure of the array manifold matrix into account, the extended matrix can be turned into a tensor model which admits the Vandermonde constrained PARAFAC decomposition. Based on this tensor model, an efficient linear algebra algorithm is applied to obtain the DOA estimation via utilizing the rotational invariance between two submatrices. Compared with some existing algorithms, the proposed method has a better DOA estimation performance. Meanwhile, the proposed method consistently has a higher estimation accuracy and a much lower computational complexity than the trilinear alternating least square- (TALS-) based PARAFAC algorithm. Finally, numerical examples are conducted to demonstrate the effectiveness of the proposed approach in terms of estimation accuracy and computational complexity.
url http://dx.doi.org/10.1155/2018/4612583
work_keys_str_mv AT heyunlin estimationofdoafornoncircularsignalsviavandermondeconstrainedparallelfactoranalysis
AT chaoweiyuan estimationofdoafornoncircularsignalsviavandermondeconstrainedparallelfactoranalysis
AT jianhedu estimationofdoafornoncircularsignalsviavandermondeconstrainedparallelfactoranalysis
AT zhongweihu estimationofdoafornoncircularsignalsviavandermondeconstrainedparallelfactoranalysis
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