The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
O uso de intervalos de tempo locais para tratar sinais não estacionários do mundo real como estacionários produz sinais quase estacionários (QSS). Neste artigo, a estimativa da direção de chegada (DOA) de QSS não circular não correlacionado é analisada aplicando uma nova técnica para obter defasagens consecutivas maiores usando array coprime. É proposto um esquema de extensão virtual do array coprime que explora a diferença e a soma do co-array que pode aumentar as defasagens consecutivas do co-array em um número notável, usando menos número de sensores. No método proposto, defasagens cruzadas e também defasagens próprias são exploradas para extensão virtual de co-matrizes tanto para diferenças quanto para somas. O método oferece maiores graus de liberdade (DOF) com um maior número de defasagens consecutivas não negativas iguais a MN+2M+1 usando apenas M+N-1 número de sensores onde M e N são coprimos com espaçamentos entre elementos adequados. Uma matriz de covariância maior pode ser alcançada realizando cálculos semelhantes a covariância com a abordagem baseada no subespaço Khatri-Rao (KR), que pode operar em casos indeterminados e até mesmo lidar com covariâncias de ruído desconhecidas. Este artigo concentra-se apenas em defasagens consecutivas não negativas e um método baseado em subespaço, como a abordagem baseada em Classificação de Sinais Múltiplos (MÚSICA), foi executado para estimativa de DOA. Assim, o método proposto, denominado Extensão Virtual de Coprime Array absorvendo Diferença e Soma (VECADS), neste trabalho é promissor para criar matriz de covariância maior com maior DOF para estimativa de DOA de alta resolução. A distribuição coprime produzida pela abordagem proposta pode produzir estimativas de DOA de maior resolução, evitando o efeito de acoplamento mútuo. Os resultados da simulação demonstram sua eficácia em termos de precisão da estimativa de DOA, mesmo com fontes estreitamente alinhadas usando menos sensores em comparação com outras técnicas, como protótipo coprime, coprime convencional, Coprime Array with Displaced Subarrays (CADiS), CADiS após Coprime Array com Compressed Inter-element Espaçamento (CACIS) e array aninhado capturando apenas co-array de diferença.
Tarek Hasan AL MAHMUD
University of Science and Technology of China
Zhongfu YE
University of Science and Technology of China
Kashif SHABIR
University of Science and Technology of China
Yawar Ali SHEIKH
University of Science and Technology of China
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copiar
Tarek Hasan AL MAHMUD, Zhongfu YE, Kashif SHABIR, Yawar Ali SHEIKH, "DOA Estimation of Quasi-Stationary Signals Exploiting Virtual Extension of Coprime Array Imbibing Difference and Sum Co-Array" in IEICE TRANSACTIONS on Communications,
vol. E101-B, no. 8, pp. 1876-1883, August 2018, doi: 10.1587/transcom.2017EBP3375.
Abstract: Using local time frames to treat non-stationary real world signals as stationary yields Quasi-Stationary Signals (QSS). In this paper, direction of arrival (DOA) estimation of uncorrelated non-circular QSS is analyzed by applying a novel technique to achieve larger consecutive lags using coprime array. A scheme of virtual extension of coprime array is proposed that exploits the difference and sum co-array which can increase consecutive co-array lags in remarkable number by using less number of sensors. In the proposed method, cross lags as well as self lags are exploited for virtual extension of co-arrays both for differences and sums. The method offers higher degrees of freedom (DOF) with a larger number of non-negative consecutive lags equal to MN+2M+1 by using only M+N-1 number of sensors where M and N are coprime with congenial interelement spacings. A larger covariance matrix can be achieved by performing covariance like computations with the Khatri-Rao (KR) subspace based approach which can operate in undetermined cases and even can deal with unknown noise covariances. This paper concentrates on only non-negative consecutive lags and subspace based method like Multiple Signal Classification (MUSIC) based approach has been executed for DOA estimation. Hence, the proposed method, named Virtual Extension of Coprime Array imbibing Difference and Sum (VECADS), in this work is promising to create larger covariance matrix with higher DOF for high resolution DOA estimation. The coprime distribution yielded by the proposed approach can yield higher resolution DOA estimation while avoiding the mutual coupling effect. Simulation results demonstrate its effectiveness in terms of the accuracy of DOA estimation even with tightly aligned sources using fewer sensors compared with other techniques like prototype coprime, conventional coprime, Coprime Array with Displaced Subarrays (CADiS), CADiS after Coprime Array with Compressed Inter-element Spacing (CACIS) and nested array seizing only difference co-array.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2017EBP3375/_p
Copiar
@ARTICLE{e101-b_8_1876,
author={Tarek Hasan AL MAHMUD, Zhongfu YE, Kashif SHABIR, Yawar Ali SHEIKH, },
journal={IEICE TRANSACTIONS on Communications},
title={DOA Estimation of Quasi-Stationary Signals Exploiting Virtual Extension of Coprime Array Imbibing Difference and Sum Co-Array},
year={2018},
volume={E101-B},
number={8},
pages={1876-1883},
abstract={Using local time frames to treat non-stationary real world signals as stationary yields Quasi-Stationary Signals (QSS). In this paper, direction of arrival (DOA) estimation of uncorrelated non-circular QSS is analyzed by applying a novel technique to achieve larger consecutive lags using coprime array. A scheme of virtual extension of coprime array is proposed that exploits the difference and sum co-array which can increase consecutive co-array lags in remarkable number by using less number of sensors. In the proposed method, cross lags as well as self lags are exploited for virtual extension of co-arrays both for differences and sums. The method offers higher degrees of freedom (DOF) with a larger number of non-negative consecutive lags equal to MN+2M+1 by using only M+N-1 number of sensors where M and N are coprime with congenial interelement spacings. A larger covariance matrix can be achieved by performing covariance like computations with the Khatri-Rao (KR) subspace based approach which can operate in undetermined cases and even can deal with unknown noise covariances. This paper concentrates on only non-negative consecutive lags and subspace based method like Multiple Signal Classification (MUSIC) based approach has been executed for DOA estimation. Hence, the proposed method, named Virtual Extension of Coprime Array imbibing Difference and Sum (VECADS), in this work is promising to create larger covariance matrix with higher DOF for high resolution DOA estimation. The coprime distribution yielded by the proposed approach can yield higher resolution DOA estimation while avoiding the mutual coupling effect. Simulation results demonstrate its effectiveness in terms of the accuracy of DOA estimation even with tightly aligned sources using fewer sensors compared with other techniques like prototype coprime, conventional coprime, Coprime Array with Displaced Subarrays (CADiS), CADiS after Coprime Array with Compressed Inter-element Spacing (CACIS) and nested array seizing only difference co-array.},
keywords={},
doi={10.1587/transcom.2017EBP3375},
ISSN={1745-1345},
month={August},}
Copiar
TY - JOUR
TI - DOA Estimation of Quasi-Stationary Signals Exploiting Virtual Extension of Coprime Array Imbibing Difference and Sum Co-Array
T2 - IEICE TRANSACTIONS on Communications
SP - 1876
EP - 1883
AU - Tarek Hasan AL MAHMUD
AU - Zhongfu YE
AU - Kashif SHABIR
AU - Yawar Ali SHEIKH
PY - 2018
DO - 10.1587/transcom.2017EBP3375
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E101-B
IS - 8
JA - IEICE TRANSACTIONS on Communications
Y1 - August 2018
AB - Using local time frames to treat non-stationary real world signals as stationary yields Quasi-Stationary Signals (QSS). In this paper, direction of arrival (DOA) estimation of uncorrelated non-circular QSS is analyzed by applying a novel technique to achieve larger consecutive lags using coprime array. A scheme of virtual extension of coprime array is proposed that exploits the difference and sum co-array which can increase consecutive co-array lags in remarkable number by using less number of sensors. In the proposed method, cross lags as well as self lags are exploited for virtual extension of co-arrays both for differences and sums. The method offers higher degrees of freedom (DOF) with a larger number of non-negative consecutive lags equal to MN+2M+1 by using only M+N-1 number of sensors where M and N are coprime with congenial interelement spacings. A larger covariance matrix can be achieved by performing covariance like computations with the Khatri-Rao (KR) subspace based approach which can operate in undetermined cases and even can deal with unknown noise covariances. This paper concentrates on only non-negative consecutive lags and subspace based method like Multiple Signal Classification (MUSIC) based approach has been executed for DOA estimation. Hence, the proposed method, named Virtual Extension of Coprime Array imbibing Difference and Sum (VECADS), in this work is promising to create larger covariance matrix with higher DOF for high resolution DOA estimation. The coprime distribution yielded by the proposed approach can yield higher resolution DOA estimation while avoiding the mutual coupling effect. Simulation results demonstrate its effectiveness in terms of the accuracy of DOA estimation even with tightly aligned sources using fewer sensors compared with other techniques like prototype coprime, conventional coprime, Coprime Array with Displaced Subarrays (CADiS), CADiS after Coprime Array with Compressed Inter-element Spacing (CACIS) and nested array seizing only difference co-array.
ER -