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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Series: | International Journal of Antennas and Propagation |
Online Access: | http://dx.doi.org/10.1155/2018/4612583 |
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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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1725958101769650176 |