Off-Grid DOA Estimation Using Sparse Bayesian Learning for MIMO Radar under Impulsive Noise
Direction of arrival (DOA) estimation is an essential and fundamental part of array signal processing, which has been widely used in radio monitoring, autonomous driving of vehicles, intelligent navigation, etc. However, it remains a challenge to accurately estimate DOA for multiple-input multiple-o...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/16/6268 |
| _version_ | 1797442046043095040 |
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| author | Jitong Ma Jiacheng Zhang Zhengyan Yang Tianshuang Qiu |
| author_facet | Jitong Ma Jiacheng Zhang Zhengyan Yang Tianshuang Qiu |
| author_sort | Jitong Ma |
| collection | DOAJ |
| description | Direction of arrival (DOA) estimation is an essential and fundamental part of array signal processing, which has been widely used in radio monitoring, autonomous driving of vehicles, intelligent navigation, etc. However, it remains a challenge to accurately estimate DOA for multiple-input multiple-output (MIMO) radar in impulsive noise environments. To address this problem, an off-grid DOA estimation method for monostatic MIMO radar is proposed to deal with non-circular signals under impulsive noise. In the proposed method, firstly, based on the property of non-circular signal and array structure, a virtual array output was built and a real-valued sparse representation for the signal model was constructed. Then, an off-grid sparse Bayesian learning (SBL) framework is proposed and further applied to the virtual array to construct novel off-grid sparse model. Finally, off-grid DOA estimation was realized through the solution of the sparse reconstruction with high accuracy even in impulsive noise. Numerous simulations were performed to compare the algorithm with existing methods. Simulation results verify that the proposed off-grid DOA method enables evident performance improvement in terms of accuracy and robustness compared with other works on impulsive noise. |
| first_indexed | 2024-03-09T12:35:55Z |
| format | Article |
| id | doaj.art-62ecd8e93ff241d793acea71b9500fe7 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T12:35:55Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-62ecd8e93ff241d793acea71b9500fe72023-11-30T22:24:21ZengMDPI AGSensors1424-82202022-08-012216626810.3390/s22166268Off-Grid DOA Estimation Using Sparse Bayesian Learning for MIMO Radar under Impulsive NoiseJitong Ma0Jiacheng Zhang1Zhengyan Yang2Tianshuang Qiu3College of Information Science and Technology, Dalian Maritime University, Dalian 116026, ChinaSchool of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCollege of Transportation Engineering, Dalian Maritime University, Dalian 116026, ChinaDepartment of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, ChinaDirection of arrival (DOA) estimation is an essential and fundamental part of array signal processing, which has been widely used in radio monitoring, autonomous driving of vehicles, intelligent navigation, etc. However, it remains a challenge to accurately estimate DOA for multiple-input multiple-output (MIMO) radar in impulsive noise environments. To address this problem, an off-grid DOA estimation method for monostatic MIMO radar is proposed to deal with non-circular signals under impulsive noise. In the proposed method, firstly, based on the property of non-circular signal and array structure, a virtual array output was built and a real-valued sparse representation for the signal model was constructed. Then, an off-grid sparse Bayesian learning (SBL) framework is proposed and further applied to the virtual array to construct novel off-grid sparse model. Finally, off-grid DOA estimation was realized through the solution of the sparse reconstruction with high accuracy even in impulsive noise. Numerous simulations were performed to compare the algorithm with existing methods. Simulation results verify that the proposed off-grid DOA method enables evident performance improvement in terms of accuracy and robustness compared with other works on impulsive noise.https://www.mdpi.com/1424-8220/22/16/6268DOA estimationmonostatic MIMO radarimpulsive noisesparse Bayesian learning |
| spellingShingle | Jitong Ma Jiacheng Zhang Zhengyan Yang Tianshuang Qiu Off-Grid DOA Estimation Using Sparse Bayesian Learning for MIMO Radar under Impulsive Noise Sensors DOA estimation monostatic MIMO radar impulsive noise sparse Bayesian learning |
| title | Off-Grid DOA Estimation Using Sparse Bayesian Learning for MIMO Radar under Impulsive Noise |
| title_full | Off-Grid DOA Estimation Using Sparse Bayesian Learning for MIMO Radar under Impulsive Noise |
| title_fullStr | Off-Grid DOA Estimation Using Sparse Bayesian Learning for MIMO Radar under Impulsive Noise |
| title_full_unstemmed | Off-Grid DOA Estimation Using Sparse Bayesian Learning for MIMO Radar under Impulsive Noise |
| title_short | Off-Grid DOA Estimation Using Sparse Bayesian Learning for MIMO Radar under Impulsive Noise |
| title_sort | off grid doa estimation using sparse bayesian learning for mimo radar under impulsive noise |
| topic | DOA estimation monostatic MIMO radar impulsive noise sparse Bayesian learning |
| url | https://www.mdpi.com/1424-8220/22/16/6268 |
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