A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW Radar
A Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous Wave (FMCW) radar can provide a range-angle map that expresses the signal power against each range and angle. It is possible to estimate object locations by detecting the signal power that exceeds a threshold using an algorithm,...
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| Format: | Article |
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MDPI AG
2022-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/23/9401 |
| _version_ | 1797462136110186496 |
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| author | Koji Endo Kohei Yamamoto Tomoaki Ohtsuki |
| author_facet | Koji Endo Kohei Yamamoto Tomoaki Ohtsuki |
| author_sort | Koji Endo |
| collection | DOAJ |
| description | A Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous Wave (FMCW) radar can provide a range-angle map that expresses the signal power against each range and angle. It is possible to estimate object locations by detecting the signal power that exceeds a threshold using an algorithm, such as Constant False Alarm Rate (CFAR). However, noise and multipath components often exist over the range-angle map, which could produce false alarms for an undesired location depending on the threshold setting. In other words, the threshold setting is sensitive in noisy range-angle maps. Therefore, if the noise is reduced, the threshold can be easily set to reduce the number of false alarms. In this paper, we propose a method that improves the CFAR threshold tolerance by denoising a range-angle map using Deep Image Prior (DIP). DIP is an unsupervised deep-learning technique that enables image denoising. In the proposed method, DIP is applied to the range-angle map calculated by the Curve-Length (CL) method, and then the object location is detected over the denoised range-angle map based on Cell-Averaging CFAR (CA-CFAR), which is a typical threshold setting algorithm. Through the experiments to estimate human locations in indoor environments, we confirmed that the proposed method with DIP reduced the number of false alarms and estimated the human location accurately while improving the tolerance of the threshold setting, compared to the method without DIP. |
| first_indexed | 2024-03-09T17:32:12Z |
| format | Article |
| id | doaj.art-c000893401094f5e900fe502af0dda64 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T17:32:12Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-c000893401094f5e900fe502af0dda642023-11-24T12:13:39ZengMDPI AGSensors1424-82202022-12-012223940110.3390/s22239401A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW RadarKoji Endo0Kohei Yamamoto1Tomoaki Ohtsuki2Graduate School of Science and Technology, Keio University, Yokohama 223-8522, JapanDepartment of Information and Computer Science, Faculty of Science and Technology, Keio University, Yokohama 223-8522, JapanDepartment of Information and Computer Science, Faculty of Science and Technology, Keio University, Yokohama 223-8522, JapanA Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous Wave (FMCW) radar can provide a range-angle map that expresses the signal power against each range and angle. It is possible to estimate object locations by detecting the signal power that exceeds a threshold using an algorithm, such as Constant False Alarm Rate (CFAR). However, noise and multipath components often exist over the range-angle map, which could produce false alarms for an undesired location depending on the threshold setting. In other words, the threshold setting is sensitive in noisy range-angle maps. Therefore, if the noise is reduced, the threshold can be easily set to reduce the number of false alarms. In this paper, we propose a method that improves the CFAR threshold tolerance by denoising a range-angle map using Deep Image Prior (DIP). DIP is an unsupervised deep-learning technique that enables image denoising. In the proposed method, DIP is applied to the range-angle map calculated by the Curve-Length (CL) method, and then the object location is detected over the denoised range-angle map based on Cell-Averaging CFAR (CA-CFAR), which is a typical threshold setting algorithm. Through the experiments to estimate human locations in indoor environments, we confirmed that the proposed method with DIP reduced the number of false alarms and estimated the human location accurately while improving the tolerance of the threshold setting, compared to the method without DIP.https://www.mdpi.com/1424-8220/22/23/9401radardenoisingdeep image prior |
| spellingShingle | Koji Endo Kohei Yamamoto Tomoaki Ohtsuki A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW Radar Sensors radar denoising deep image prior |
| title | A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW Radar |
| title_full | A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW Radar |
| title_fullStr | A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW Radar |
| title_full_unstemmed | A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW Radar |
| title_short | A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW Radar |
| title_sort | denoising method using deep image prior to human target detection using mimo fmcw radar |
| topic | radar denoising deep image prior |
| url | https://www.mdpi.com/1424-8220/22/23/9401 |
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