A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar
It is critical to estimate and eliminate the wavelets of ground penetrating radar (GPR), so as to optimally compensate the energy attenuation and phase distortion. This paper presents a new wavelet extraction method based on a two-layer Long Short-Term Memory (LSTM) network. It only uses several ran...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/13/6457 |
| _version_ | 1797481040699195392 |
|---|---|
| author | Jianrong Geng Juan He Hongxia Ye Bin Zhan |
| author_facet | Jianrong Geng Juan He Hongxia Ye Bin Zhan |
| author_sort | Jianrong Geng |
| collection | DOAJ |
| description | It is critical to estimate and eliminate the wavelets of ground penetrating radar (GPR), so as to optimally compensate the energy attenuation and phase distortion. This paper presents a new wavelet extraction method based on a two-layer Long Short-Term Memory (LSTM) network. It only uses several random A-scan echoes (i.e., single channel detection echo sequence) to accurately predict the wavelet of any scene. The layered detection scenes with objects buried in different region are set for the 3D Finite-Difference Time-Domain simulator to generate radar echoes as a dataset. Additionally, the simulation echoes of different scenes are used to test the performance of the neural network. Multiple experiments indicate that the trained network can directly predict the wavelets quickly and accurately, although the simulation environment becomes quite different. Moreover, the measured data collected by the Qingdao Radio Research Institute radar and the unmanned aerial vehicle ground penetrating radar are used for test. The predicted wavelets can perfectly offset the original data. Therefore, the presented LSTM network can effectively predict the wavelets and their tailing oscillations for different detection scenes. The LSTM network has obvious advantages compared with other wavelet extraction methods in practical engineering. |
| first_indexed | 2024-03-09T22:08:47Z |
| format | Article |
| id | doaj.art-c29cdd26a16c4bcb81a2d69d763072da |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:08:47Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c29cdd26a16c4bcb81a2d69d763072da2023-11-23T19:36:59ZengMDPI AGApplied Sciences2076-34172022-06-011213645710.3390/app12136457A Clutter Suppression Method Based on LSTM Network for Ground Penetrating RadarJianrong Geng0Juan He1Hongxia Ye2Bin Zhan3Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, ChinaKey Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, ChinaKey Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, ChinaNVIDIA Technology Shanghai Co., Ltd., Shanghai 201210, ChinaIt is critical to estimate and eliminate the wavelets of ground penetrating radar (GPR), so as to optimally compensate the energy attenuation and phase distortion. This paper presents a new wavelet extraction method based on a two-layer Long Short-Term Memory (LSTM) network. It only uses several random A-scan echoes (i.e., single channel detection echo sequence) to accurately predict the wavelet of any scene. The layered detection scenes with objects buried in different region are set for the 3D Finite-Difference Time-Domain simulator to generate radar echoes as a dataset. Additionally, the simulation echoes of different scenes are used to test the performance of the neural network. Multiple experiments indicate that the trained network can directly predict the wavelets quickly and accurately, although the simulation environment becomes quite different. Moreover, the measured data collected by the Qingdao Radio Research Institute radar and the unmanned aerial vehicle ground penetrating radar are used for test. The predicted wavelets can perfectly offset the original data. Therefore, the presented LSTM network can effectively predict the wavelets and their tailing oscillations for different detection scenes. The LSTM network has obvious advantages compared with other wavelet extraction methods in practical engineering.https://www.mdpi.com/2076-3417/12/13/6457ground penetrating radarlong short-term memory networkwavelet extraction |
| spellingShingle | Jianrong Geng Juan He Hongxia Ye Bin Zhan A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar Applied Sciences ground penetrating radar long short-term memory network wavelet extraction |
| title | A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar |
| title_full | A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar |
| title_fullStr | A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar |
| title_full_unstemmed | A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar |
| title_short | A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar |
| title_sort | clutter suppression method based on lstm network for ground penetrating radar |
| topic | ground penetrating radar long short-term memory network wavelet extraction |
| url | https://www.mdpi.com/2076-3417/12/13/6457 |
| work_keys_str_mv | AT jianronggeng acluttersuppressionmethodbasedonlstmnetworkforgroundpenetratingradar AT juanhe acluttersuppressionmethodbasedonlstmnetworkforgroundpenetratingradar AT hongxiaye acluttersuppressionmethodbasedonlstmnetworkforgroundpenetratingradar AT binzhan acluttersuppressionmethodbasedonlstmnetworkforgroundpenetratingradar AT jianronggeng cluttersuppressionmethodbasedonlstmnetworkforgroundpenetratingradar AT juanhe cluttersuppressionmethodbasedonlstmnetworkforgroundpenetratingradar AT hongxiaye cluttersuppressionmethodbasedonlstmnetworkforgroundpenetratingradar AT binzhan cluttersuppressionmethodbasedonlstmnetworkforgroundpenetratingradar |