2D-DOA and Polarization Estimation Using a Novel Sparse Representation of Covariance Matrix With COLD Array
In this paper, we propose a novel sparse signal representation (SSR)-based algorithm called the <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula>-PSRCM with cocentered orthogonal loop and dipole (COLD) array to estimate two-dimensi...
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8519728/ |
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doaj-720e63a4089243c4abb8c683f76004612021-03-29T20:27:09ZengIEEEIEEE Access2169-35362018-01-016663856639510.1109/ACCESS.2018.287905185197282D-DOA and Polarization Estimation Using a Novel Sparse Representation of Covariance Matrix With COLD ArrayWeijian Si0Yan Wang1https://orcid.org/0000-0003-3565-8323Chunjie Zhang2College of Information and Communication Engineering, Harbin Engineering University, Harbin, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin, ChinaIn this paper, we propose a novel sparse signal representation (SSR)-based algorithm called the <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula>-PSRCM with cocentered orthogonal loop and dipole (COLD) array to estimate two-dimensional (2D) direction of arrival (DOA) and polarization parameters. Considering the characteristics of polarization sensors, a polarized sparse representation model of covariance matrix is constructed, whose overcomplete dictionary and sparse coefficient matrix only depend on DOA and polarization parameters, respectively. In so doing, the proposed algorithm can make full use of the spatial and polarized information contained in the received data, thereby improving the estimation accuracy. In addition, to reduce the computational complexity and suppress the effect of noise, the modified <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula>-PSRCM algorithm with the real-valued sparse coefficient matrix and noise-free sparse representation model is proposed. Finally, we present the two-dimensional multiresolution grid refinement (2D-MGR) method to reduce the heavy computation burden when the spatial grid is dense. Simulation results validate the superiority of the proposed algorithms.https://ieeexplore.ieee.org/document/8519728/Direction of arrivalℓ⠁-norm penaltypolarization sensitive arraysignal processingspares signal representationtwo-dimensional multi-resolution grid refinement |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Weijian Si Yan Wang Chunjie Zhang |
| spellingShingle |
Weijian Si Yan Wang Chunjie Zhang 2D-DOA and Polarization Estimation Using a Novel Sparse Representation of Covariance Matrix With COLD Array IEEE Access Direction of arrival ℓ⠁-norm penalty polarization sensitive array signal processing spares signal representation two-dimensional multi-resolution grid refinement |
| author_facet |
Weijian Si Yan Wang Chunjie Zhang |
| author_sort |
Weijian Si |
| title |
2D-DOA and Polarization Estimation Using a Novel Sparse Representation of Covariance Matrix With COLD Array |
| title_short |
2D-DOA and Polarization Estimation Using a Novel Sparse Representation of Covariance Matrix With COLD Array |
| title_full |
2D-DOA and Polarization Estimation Using a Novel Sparse Representation of Covariance Matrix With COLD Array |
| title_fullStr |
2D-DOA and Polarization Estimation Using a Novel Sparse Representation of Covariance Matrix With COLD Array |
| title_full_unstemmed |
2D-DOA and Polarization Estimation Using a Novel Sparse Representation of Covariance Matrix With COLD Array |
| title_sort |
2d-doa and polarization estimation using a novel sparse representation of covariance matrix with cold array |
| publisher |
IEEE |
| series |
IEEE Access |
| issn |
2169-3536 |
| publishDate |
2018-01-01 |
| description |
In this paper, we propose a novel sparse signal representation (SSR)-based algorithm called the <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula>-PSRCM with cocentered orthogonal loop and dipole (COLD) array to estimate two-dimensional (2D) direction of arrival (DOA) and polarization parameters. Considering the characteristics of polarization sensors, a polarized sparse representation model of covariance matrix is constructed, whose overcomplete dictionary and sparse coefficient matrix only depend on DOA and polarization parameters, respectively. In so doing, the proposed algorithm can make full use of the spatial and polarized information contained in the received data, thereby improving the estimation accuracy. In addition, to reduce the computational complexity and suppress the effect of noise, the modified <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula>-PSRCM algorithm with the real-valued sparse coefficient matrix and noise-free sparse representation model is proposed. Finally, we present the two-dimensional multiresolution grid refinement (2D-MGR) method to reduce the heavy computation burden when the spatial grid is dense. Simulation results validate the superiority of the proposed algorithms. |
| topic |
Direction of arrival ℓ⠁-norm penalty polarization sensitive array signal processing spares signal representation two-dimensional multi-resolution grid refinement |
| url |
https://ieeexplore.ieee.org/document/8519728/ |
| work_keys_str_mv |
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