DOA and Range Estimation for FDA-MIMO Radar with Sparse Bayesian Learning
Due to grid division, the existing target localization algorithms based on sparse signal recovery for the frequency diverse array multiple-input multiple-output (FDA-MIMO) radar not only suffer from high computational complexity but also encounter significant estimation performance degradation cause...
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MDPI AG
2021-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/13/2553 |
| _version_ | 1797528320274857984 |
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| author | Qi Liu Xianpeng Wang Mengxing Huang Xiang Lan Lu Sun |
| author_facet | Qi Liu Xianpeng Wang Mengxing Huang Xiang Lan Lu Sun |
| author_sort | Qi Liu |
| collection | DOAJ |
| description | Due to grid division, the existing target localization algorithms based on sparse signal recovery for the frequency diverse array multiple-input multiple-output (FDA-MIMO) radar not only suffer from high computational complexity but also encounter significant estimation performance degradation caused by off-grid gaps. To tackle the aforementioned problems, an effective off-grid Sparse Bayesian Learning (SBL) method is proposed in this paper, which enables the calculation the direction of arrival (DOA) and range estimates. First of all, the angle-dependent component is split by reconstructing the received data and contributes to immediately extract rough DOA estimates with the root SBL algorithm, which, subsequently, are utilized to obtain the paired rough range estimates. Furthermore, a discrete grid is constructed by the rough DOA and range estimates, and the 2D-SBL model is proposed to optimize the rough DOA and range estimates. Moreover, the expectation-maximization (EM) algorithm is utilized to update the grid points iteratively to further eliminate the errors caused by the off-grid model. Finally, theoretical analyses and numerical simulations illustrate the effectiveness and superiority of the proposed method. |
| first_indexed | 2024-03-10T09:56:35Z |
| format | Article |
| id | doaj.art-373d572f6f9e4e6f8f44fb6d39f6d173 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T09:56:35Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-373d572f6f9e4e6f8f44fb6d39f6d1732023-11-22T02:17:19ZengMDPI AGRemote Sensing2072-42922021-06-011313255310.3390/rs13132553DOA and Range Estimation for FDA-MIMO Radar with Sparse Bayesian LearningQi Liu0Xianpeng Wang1Mengxing Huang2Xiang Lan3Lu Sun4State Key Laboratory of Marine Resource Utilization in South China Sea, School of Information and Communication Engineering, Hainan University, Haikou 570228, ChinaState Key Laboratory of Marine Resource Utilization in South China Sea, School of Information and Communication Engineering, Hainan University, Haikou 570228, ChinaState Key Laboratory of Marine Resource Utilization in South China Sea, School of Information and Communication Engineering, Hainan University, Haikou 570228, ChinaState Key Laboratory of Marine Resource Utilization in South China Sea, School of Information and Communication Engineering, Hainan University, Haikou 570228, ChinaDepartment of Communication Engineering, Institute of Information Science Technology, Dalian Maritime University, Dalian 116086, ChinaDue to grid division, the existing target localization algorithms based on sparse signal recovery for the frequency diverse array multiple-input multiple-output (FDA-MIMO) radar not only suffer from high computational complexity but also encounter significant estimation performance degradation caused by off-grid gaps. To tackle the aforementioned problems, an effective off-grid Sparse Bayesian Learning (SBL) method is proposed in this paper, which enables the calculation the direction of arrival (DOA) and range estimates. First of all, the angle-dependent component is split by reconstructing the received data and contributes to immediately extract rough DOA estimates with the root SBL algorithm, which, subsequently, are utilized to obtain the paired rough range estimates. Furthermore, a discrete grid is constructed by the rough DOA and range estimates, and the 2D-SBL model is proposed to optimize the rough DOA and range estimates. Moreover, the expectation-maximization (EM) algorithm is utilized to update the grid points iteratively to further eliminate the errors caused by the off-grid model. Finally, theoretical analyses and numerical simulations illustrate the effectiveness and superiority of the proposed method.https://www.mdpi.com/2072-4292/13/13/2553target localizationFDA-MIMO radarDOA and range estimationsparse Bayesian learning |
| spellingShingle | Qi Liu Xianpeng Wang Mengxing Huang Xiang Lan Lu Sun DOA and Range Estimation for FDA-MIMO Radar with Sparse Bayesian Learning Remote Sensing target localization FDA-MIMO radar DOA and range estimation sparse Bayesian learning |
| title | DOA and Range Estimation for FDA-MIMO Radar with Sparse Bayesian Learning |
| title_full | DOA and Range Estimation for FDA-MIMO Radar with Sparse Bayesian Learning |
| title_fullStr | DOA and Range Estimation for FDA-MIMO Radar with Sparse Bayesian Learning |
| title_full_unstemmed | DOA and Range Estimation for FDA-MIMO Radar with Sparse Bayesian Learning |
| title_short | DOA and Range Estimation for FDA-MIMO Radar with Sparse Bayesian Learning |
| title_sort | doa and range estimation for fda mimo radar with sparse bayesian learning |
| topic | target localization FDA-MIMO radar DOA and range estimation sparse Bayesian learning |
| url | https://www.mdpi.com/2072-4292/13/13/2553 |
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