The Real-Valued Sparse Direction of Arrival (DOA) Estimation Based on the Khatri-Rao Product
There is a problem that complex operation which leads to a heavy calculation burden is required when the direction of arrival (DOA) of a sparse signal is estimated by using the array covariance matrix. The solution of the multiple measurement vectors (MMV) model is difficult. In this paper, a real-v...
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| Format: | Article |
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MDPI AG
2016-05-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/5/693 |
| _version_ | 1798005319263059968 |
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| author | Tao Chen Huanxin Wu Zhongkai Zhao |
| author_facet | Tao Chen Huanxin Wu Zhongkai Zhao |
| author_sort | Tao Chen |
| collection | DOAJ |
| description | There is a problem that complex operation which leads to a heavy calculation burden is required when the direction of arrival (DOA) of a sparse signal is estimated by using the array covariance matrix. The solution of the multiple measurement vectors (MMV) model is difficult. In this paper, a real-valued sparse DOA estimation algorithm based on the Khatri-Rao (KR) product called the L1-RVSKR is proposed. The proposed algorithm is based on the sparse representation of the array covariance matrix. The array covariance matrix is transformed to a real-valued matrix via a unitary transformation so that a real-valued sparse model is achieved. The real-valued sparse model is vectorized for transforming to a single measurement vector (SMV) model, and a new virtual overcomplete dictionary is constructed according to the KR product’s property. Finally, the sparse DOA estimation is solved by utilizing the idea of a sparse representation of array covariance vectors (SRACV). The simulation results demonstrate the superior performance and the low computational complexity of the proposed algorithm. |
| first_indexed | 2024-04-11T12:37:19Z |
| format | Article |
| id | doaj.art-072351b8be0549be804d6517025f11f1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:37:19Z |
| publishDate | 2016-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-072351b8be0549be804d6517025f11f12022-12-22T04:23:35ZengMDPI AGSensors1424-82202016-05-0116569310.3390/s16050693s16050693The Real-Valued Sparse Direction of Arrival (DOA) Estimation Based on the Khatri-Rao ProductTao Chen0Huanxin Wu1Zhongkai Zhao2College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaThere is a problem that complex operation which leads to a heavy calculation burden is required when the direction of arrival (DOA) of a sparse signal is estimated by using the array covariance matrix. The solution of the multiple measurement vectors (MMV) model is difficult. In this paper, a real-valued sparse DOA estimation algorithm based on the Khatri-Rao (KR) product called the L1-RVSKR is proposed. The proposed algorithm is based on the sparse representation of the array covariance matrix. The array covariance matrix is transformed to a real-valued matrix via a unitary transformation so that a real-valued sparse model is achieved. The real-valued sparse model is vectorized for transforming to a single measurement vector (SMV) model, and a new virtual overcomplete dictionary is constructed according to the KR product’s property. Finally, the sparse DOA estimation is solved by utilizing the idea of a sparse representation of array covariance vectors (SRACV). The simulation results demonstrate the superior performance and the low computational complexity of the proposed algorithm.http://www.mdpi.com/1424-8220/16/5/693sparse direction of arrival (DOA) estimationmultiple measurement vectors (MMV)Khatri-Rao (KR) productunitary transformationarray covariance vectors |
| spellingShingle | Tao Chen Huanxin Wu Zhongkai Zhao The Real-Valued Sparse Direction of Arrival (DOA) Estimation Based on the Khatri-Rao Product Sensors sparse direction of arrival (DOA) estimation multiple measurement vectors (MMV) Khatri-Rao (KR) product unitary transformation array covariance vectors |
| title | The Real-Valued Sparse Direction of Arrival (DOA) Estimation Based on the Khatri-Rao Product |
| title_full | The Real-Valued Sparse Direction of Arrival (DOA) Estimation Based on the Khatri-Rao Product |
| title_fullStr | The Real-Valued Sparse Direction of Arrival (DOA) Estimation Based on the Khatri-Rao Product |
| title_full_unstemmed | The Real-Valued Sparse Direction of Arrival (DOA) Estimation Based on the Khatri-Rao Product |
| title_short | The Real-Valued Sparse Direction of Arrival (DOA) Estimation Based on the Khatri-Rao Product |
| title_sort | real valued sparse direction of arrival doa estimation based on the khatri rao product |
| topic | sparse direction of arrival (DOA) estimation multiple measurement vectors (MMV) Khatri-Rao (KR) product unitary transformation array covariance vectors |
| url | http://www.mdpi.com/1424-8220/16/5/693 |
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