Multiple Stationary Human Targets Detection in Through-Wall UWB Radar Based on Convolutional Neural Network
Ultra-wideband (UWB) impulse radar is widely used for through-wall human detection due to its high-range resolution and high penetration capability. UWB impulse radar can detect human targets in non-line-of-sight (NLOS) conditions, mainly based on the chest motion caused by human respiration. The au...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/9/4720 |
| _version_ | 1827673097670492160 |
|---|---|
| author | Cheng Shi Zhijie Zheng Jun Pan Zhi-Kang Ni Shengbo Ye Guangyou Fang |
| author_facet | Cheng Shi Zhijie Zheng Jun Pan Zhi-Kang Ni Shengbo Ye Guangyou Fang |
| author_sort | Cheng Shi |
| collection | DOAJ |
| description | Ultra-wideband (UWB) impulse radar is widely used for through-wall human detection due to its high-range resolution and high penetration capability. UWB impulse radar can detect human targets in non-line-of-sight (NLOS) conditions, mainly based on the chest motion caused by human respiration. The automatic detection and extraction of multiple stationary human targets is still a challenge. Missed alarms often exist if the detection method is based on the energy of the human target. This is mainly because factors such as the range of the target, the intensity of the respiratory movement, and the shadow effect will make a difference between the energy scattered by targets. Weak targets are easily masked by strong targets and thus cannot be detected. Therefore, in this paper, a multiple stationary human targets detection method based on convolutional neural network (CNN) in through-wall UWB impulse radar is proposed. After performing the signal-to-clutter-and-noise ratio (SCNR) enhancement method on the raw radar data, the range-slow-time matrix is fed into a six-layer CNN. Benefiting from the powerful feature extraction capability of CNN, the target point of interest (TPOI) can be extracted from the data matrix. The clustering algorithm is used to simplify the TPOIs to achieve accurate detection of multiple targets behind the wall. The effectiveness of the proposed method is verified by the experimental data. |
| first_indexed | 2024-03-10T04:20:12Z |
| format | Article |
| id | doaj.art-a8881efbbe5d4a68974a58f6f25ac8c0 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:20:12Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a8881efbbe5d4a68974a58f6f25ac8c02023-11-23T07:53:00ZengMDPI AGApplied Sciences2076-34172022-05-01129472010.3390/app12094720Multiple Stationary Human Targets Detection in Through-Wall UWB Radar Based on Convolutional Neural NetworkCheng Shi0Zhijie Zheng1Jun Pan2Zhi-Kang Ni3Shengbo Ye4Guangyou Fang5Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, ChinaUltra-wideband (UWB) impulse radar is widely used for through-wall human detection due to its high-range resolution and high penetration capability. UWB impulse radar can detect human targets in non-line-of-sight (NLOS) conditions, mainly based on the chest motion caused by human respiration. The automatic detection and extraction of multiple stationary human targets is still a challenge. Missed alarms often exist if the detection method is based on the energy of the human target. This is mainly because factors such as the range of the target, the intensity of the respiratory movement, and the shadow effect will make a difference between the energy scattered by targets. Weak targets are easily masked by strong targets and thus cannot be detected. Therefore, in this paper, a multiple stationary human targets detection method based on convolutional neural network (CNN) in through-wall UWB impulse radar is proposed. After performing the signal-to-clutter-and-noise ratio (SCNR) enhancement method on the raw radar data, the range-slow-time matrix is fed into a six-layer CNN. Benefiting from the powerful feature extraction capability of CNN, the target point of interest (TPOI) can be extracted from the data matrix. The clustering algorithm is used to simplify the TPOIs to achieve accurate detection of multiple targets behind the wall. The effectiveness of the proposed method is verified by the experimental data.https://www.mdpi.com/2076-3417/12/9/4720convolutional neural networkmultiple stationary human targetsthrough-wall detectionultra-wideband (UWB) impulse radar |
| spellingShingle | Cheng Shi Zhijie Zheng Jun Pan Zhi-Kang Ni Shengbo Ye Guangyou Fang Multiple Stationary Human Targets Detection in Through-Wall UWB Radar Based on Convolutional Neural Network Applied Sciences convolutional neural network multiple stationary human targets through-wall detection ultra-wideband (UWB) impulse radar |
| title | Multiple Stationary Human Targets Detection in Through-Wall UWB Radar Based on Convolutional Neural Network |
| title_full | Multiple Stationary Human Targets Detection in Through-Wall UWB Radar Based on Convolutional Neural Network |
| title_fullStr | Multiple Stationary Human Targets Detection in Through-Wall UWB Radar Based on Convolutional Neural Network |
| title_full_unstemmed | Multiple Stationary Human Targets Detection in Through-Wall UWB Radar Based on Convolutional Neural Network |
| title_short | Multiple Stationary Human Targets Detection in Through-Wall UWB Radar Based on Convolutional Neural Network |
| title_sort | multiple stationary human targets detection in through wall uwb radar based on convolutional neural network |
| topic | convolutional neural network multiple stationary human targets through-wall detection ultra-wideband (UWB) impulse radar |
| url | https://www.mdpi.com/2076-3417/12/9/4720 |
| work_keys_str_mv | AT chengshi multiplestationaryhumantargetsdetectioninthroughwalluwbradarbasedonconvolutionalneuralnetwork AT zhijiezheng multiplestationaryhumantargetsdetectioninthroughwalluwbradarbasedonconvolutionalneuralnetwork AT junpan multiplestationaryhumantargetsdetectioninthroughwalluwbradarbasedonconvolutionalneuralnetwork AT zhikangni multiplestationaryhumantargetsdetectioninthroughwalluwbradarbasedonconvolutionalneuralnetwork AT shengboye multiplestationaryhumantargetsdetectioninthroughwalluwbradarbasedonconvolutionalneuralnetwork AT guangyoufang multiplestationaryhumantargetsdetectioninthroughwalluwbradarbasedonconvolutionalneuralnetwork |