Symmetry-Imposed Rectangular Coprime and Nested Arrays for Direction of Arrival Estimation With Multiple Signal Classification
Linear sparse arrays (coprime and nested arrays) have been studied extensively as a means of performing direction of arrival (DoA) estimation while bypassing Nyquist sampling theorem. However, rectangular sparse arrays have few studies, most of which are based in lattice theory. Although the multipl...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8877708/ |
| _version_ | 1819276160294453248 |
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| author | Kaushallya Adhikari Benjamin Drozdenko |
| author_facet | Kaushallya Adhikari Benjamin Drozdenko |
| author_sort | Kaushallya Adhikari |
| collection | DOAJ |
| description | Linear sparse arrays (coprime and nested arrays) have been studied extensively as a means of performing direction of arrival (DoA) estimation while bypassing Nyquist sampling theorem. However, rectangular sparse arrays have few studies, most of which are based in lattice theory. Although the multiple signal classification (MUSIC) alogrithm can be applied to lattice theory-based sparse arrays, fewer DoAs can be estimated for these arrays than for a full array with the same aperture. One contribution of this paper is the formulation of symmetry-imposed rectangular coprime and nested array designs that have wider contiguous lags than lattice-based arrays. Also, existing algorithms for rectangular arrays employ the direct sample covariance matrix estimate, which has low accuracy. Another contribution of this paper is an alternate method for estimating the covariance matrix that ensures the matrix is block-Toeplitz. Using the proposed covariance matrix estimate, we integrate a MUSIC-based DoA estimation method that applies to both full arrays and sparse arrays. The results show that the covariance estimates produced by the proposed method have higher accuracy than the traditional sample covariance estimates. The results also demonstrate that symmetry-imposed sparse arrays have higher resolution than full rectangular arrays with the same number of sensors. |
| first_indexed | 2024-12-23T23:35:48Z |
| format | Article |
| id | doaj.art-bbf933ae445742d7b4e596b91fee1a05 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-23T23:35:48Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bbf933ae445742d7b4e596b91fee1a052022-12-21T17:25:52ZengIEEEIEEE Access2169-35362019-01-01715321715322910.1109/ACCESS.2019.29485038877708Symmetry-Imposed Rectangular Coprime and Nested Arrays for Direction of Arrival Estimation With Multiple Signal ClassificationKaushallya Adhikari0https://orcid.org/0000-0002-0706-0781Benjamin Drozdenko1Electrical Engineering Department, Louisiana Tech University, Ruston, LA, USACyber Engineering Department, Louisiana Tech University, Ruston, LA, USALinear sparse arrays (coprime and nested arrays) have been studied extensively as a means of performing direction of arrival (DoA) estimation while bypassing Nyquist sampling theorem. However, rectangular sparse arrays have few studies, most of which are based in lattice theory. Although the multiple signal classification (MUSIC) alogrithm can be applied to lattice theory-based sparse arrays, fewer DoAs can be estimated for these arrays than for a full array with the same aperture. One contribution of this paper is the formulation of symmetry-imposed rectangular coprime and nested array designs that have wider contiguous lags than lattice-based arrays. Also, existing algorithms for rectangular arrays employ the direct sample covariance matrix estimate, which has low accuracy. Another contribution of this paper is an alternate method for estimating the covariance matrix that ensures the matrix is block-Toeplitz. Using the proposed covariance matrix estimate, we integrate a MUSIC-based DoA estimation method that applies to both full arrays and sparse arrays. The results show that the covariance estimates produced by the proposed method have higher accuracy than the traditional sample covariance estimates. The results also demonstrate that symmetry-imposed sparse arrays have higher resolution than full rectangular arrays with the same number of sensors.https://ieeexplore.ieee.org/document/8877708/Coarraycoprime arraysDoA estimationMUSICnested arraysplanar arrays |
| spellingShingle | Kaushallya Adhikari Benjamin Drozdenko Symmetry-Imposed Rectangular Coprime and Nested Arrays for Direction of Arrival Estimation With Multiple Signal Classification IEEE Access Coarray coprime arrays DoA estimation MUSIC nested arrays planar arrays |
| title | Symmetry-Imposed Rectangular Coprime and Nested Arrays for Direction of Arrival Estimation With Multiple Signal Classification |
| title_full | Symmetry-Imposed Rectangular Coprime and Nested Arrays for Direction of Arrival Estimation With Multiple Signal Classification |
| title_fullStr | Symmetry-Imposed Rectangular Coprime and Nested Arrays for Direction of Arrival Estimation With Multiple Signal Classification |
| title_full_unstemmed | Symmetry-Imposed Rectangular Coprime and Nested Arrays for Direction of Arrival Estimation With Multiple Signal Classification |
| title_short | Symmetry-Imposed Rectangular Coprime and Nested Arrays for Direction of Arrival Estimation With Multiple Signal Classification |
| title_sort | symmetry imposed rectangular coprime and nested arrays for direction of arrival estimation with multiple signal classification |
| topic | Coarray coprime arrays DoA estimation MUSIC nested arrays planar arrays |
| url | https://ieeexplore.ieee.org/document/8877708/ |
| work_keys_str_mv | AT kaushallyaadhikari symmetryimposedrectangularcoprimeandnestedarraysfordirectionofarrivalestimationwithmultiplesignalclassification AT benjamindrozdenko symmetryimposedrectangularcoprimeandnestedarraysfordirectionofarrivalestimationwithmultiplesignalclassification |