Fast Target Localization in FMCW-MIMO Radar with Low SNR and Snapshot via Multi-DeepNet
Frequency modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radars are widely applied in target localization. However, during the process, the estimation accuracy decreases sharply without considerable signal-to-noise ratio (SNR) and sufficient snapshot number. It is therefore n...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/1/66 |
| _version_ | 1797431272452128768 |
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| author | Yunye Su Xiang Lan Jinmei Shi Lu Sun Xianpeng Wang |
| author_facet | Yunye Su Xiang Lan Jinmei Shi Lu Sun Xianpeng Wang |
| author_sort | Yunye Su |
| collection | DOAJ |
| description | Frequency modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radars are widely applied in target localization. However, during the process, the estimation accuracy decreases sharply without considerable signal-to-noise ratio (SNR) and sufficient snapshot number. It is therefore necessary to consider estimation schemes that are valid under low signal-to-noise ratio (SNR) and snapshot. In this paper, a fast target localization framework based on multiple deep neural networks named Multi-DeepNet is proposed. In the scheme, multiple interoperating deep networks are employed to achieve accurate target localization in harsh environments. Firstly, we designed a coarse estimate using deep learning to determine the interval where the angle is located. Then, multiple neural networks are designed to realize accurate estimation. After that, the range estimation is determined. Finally, angles and ranges are matched by comparing the Frobenius norm. Simulations and experiments are conducted to verify the efficiency and accuracy of the proposed framework. |
| first_indexed | 2024-03-09T09:41:35Z |
| format | Article |
| id | doaj.art-47a1133a2be9475d8c621cc1578a1462 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T09:41:35Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-47a1133a2be9475d8c621cc1578a14622023-12-02T00:50:45ZengMDPI AGRemote Sensing2072-42922022-12-011516610.3390/rs15010066Fast Target Localization in FMCW-MIMO Radar with Low SNR and Snapshot via Multi-DeepNetYunye Su0Xiang Lan1Jinmei Shi2Lu Sun3Xianpeng Wang4School of Information and Communication Engineering, Hainan University, Haikou 570228, ChinaSchool of Information and Communication Engineering, Hainan University, Haikou 570228, ChinaCollege of Information Engineering, Hainan Vocational University of Science and Technology, Haikou 571158, ChinaDepartment of Communication Engineering, Institute of Information Science Technology, Dalian Maritime University, Dalian 116026, ChinaSchool of Information and Communication Engineering, Hainan University, Haikou 570228, ChinaFrequency modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radars are widely applied in target localization. However, during the process, the estimation accuracy decreases sharply without considerable signal-to-noise ratio (SNR) and sufficient snapshot number. It is therefore necessary to consider estimation schemes that are valid under low signal-to-noise ratio (SNR) and snapshot. In this paper, a fast target localization framework based on multiple deep neural networks named Multi-DeepNet is proposed. In the scheme, multiple interoperating deep networks are employed to achieve accurate target localization in harsh environments. Firstly, we designed a coarse estimate using deep learning to determine the interval where the angle is located. Then, multiple neural networks are designed to realize accurate estimation. After that, the range estimation is determined. Finally, angles and ranges are matched by comparing the Frobenius norm. Simulations and experiments are conducted to verify the efficiency and accuracy of the proposed framework.https://www.mdpi.com/2072-4292/15/1/66deep learningdirection of arrival (DOA)range estimationFMCW-MIMO radarsuper-resolution estimationlocalization method |
| spellingShingle | Yunye Su Xiang Lan Jinmei Shi Lu Sun Xianpeng Wang Fast Target Localization in FMCW-MIMO Radar with Low SNR and Snapshot via Multi-DeepNet Remote Sensing deep learning direction of arrival (DOA) range estimation FMCW-MIMO radar super-resolution estimation localization method |
| title | Fast Target Localization in FMCW-MIMO Radar with Low SNR and Snapshot via Multi-DeepNet |
| title_full | Fast Target Localization in FMCW-MIMO Radar with Low SNR and Snapshot via Multi-DeepNet |
| title_fullStr | Fast Target Localization in FMCW-MIMO Radar with Low SNR and Snapshot via Multi-DeepNet |
| title_full_unstemmed | Fast Target Localization in FMCW-MIMO Radar with Low SNR and Snapshot via Multi-DeepNet |
| title_short | Fast Target Localization in FMCW-MIMO Radar with Low SNR and Snapshot via Multi-DeepNet |
| title_sort | fast target localization in fmcw mimo radar with low snr and snapshot via multi deepnet |
| topic | deep learning direction of arrival (DOA) range estimation FMCW-MIMO radar super-resolution estimation localization method |
| url | https://www.mdpi.com/2072-4292/15/1/66 |
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