A Tractor Work Position Prediction Method Based on CNN-BiLSTM Under GNSS Signal Denial
In farmland environments where GNSS signals are obstructed, such as forested areas or in adverse weather conditions, traditional GNSS/INS integrated navigation systems suffer from positioning errors and instability. To address this, a model-assisted integrated navigation system is proposed, combinin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/16/1/11 |
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| author | Yangming Hu Liyou Xu Xianghai Yan Ningjie Chang Qigang Wan Yiwei Wu |
| author_facet | Yangming Hu Liyou Xu Xianghai Yan Ningjie Chang Qigang Wan Yiwei Wu |
| author_sort | Yangming Hu |
| collection | DOAJ |
| description | In farmland environments where GNSS signals are obstructed, such as forested areas or in adverse weather conditions, traditional GNSS/INS integrated navigation systems suffer from positioning errors and instability. To address this, a model-assisted integrated navigation system is proposed, combining Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks. The CNN-BiLSTM model is trained under normal GNSS conditions and used to predict positioning when GNSS signals are interrupted, effectively replacing GNSS to ensure stable and accurate navigation. Experimental validation is conducted using field data from tractor simulations. The results show that, during a 100-s GNSS denial, the CNN-BiLSTM model reduces the average position error by 79.3% compared to pure inertial navigation and by 5.4% compared to traditional LSTM. In a 30-s GNSS denial, the average position error is reduced by 41% compared to inertial navigation and 6.2% compared to LSTM. The model maintains positioning accuracy within 3% of the GNSS/INS output under normal conditions, demonstrating its feasibility and effectiveness. This approach offers a promising solution for autonomous tractor navigation in GNSS-denied agricultural environments, contributing to precision agriculture. |
| format | Article |
| id | doaj-art-509bacf5a82c41afae06e81ea5788333 |
| institution | Kabale University |
| issn | 2032-6653 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-509bacf5a82c41afae06e81ea57883332025-01-24T13:52:45ZengMDPI AGWorld Electric Vehicle Journal2032-66532024-12-011611110.3390/wevj16010011A Tractor Work Position Prediction Method Based on CNN-BiLSTM Under GNSS Signal DenialYangming Hu0Liyou Xu1Xianghai Yan2Ningjie Chang3Qigang Wan4Yiwei Wu5College of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, ChinaCollege of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, ChinaCollege of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, ChinaCollege of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, ChinaCollege of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, ChinaCollege of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, ChinaIn farmland environments where GNSS signals are obstructed, such as forested areas or in adverse weather conditions, traditional GNSS/INS integrated navigation systems suffer from positioning errors and instability. To address this, a model-assisted integrated navigation system is proposed, combining Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks. The CNN-BiLSTM model is trained under normal GNSS conditions and used to predict positioning when GNSS signals are interrupted, effectively replacing GNSS to ensure stable and accurate navigation. Experimental validation is conducted using field data from tractor simulations. The results show that, during a 100-s GNSS denial, the CNN-BiLSTM model reduces the average position error by 79.3% compared to pure inertial navigation and by 5.4% compared to traditional LSTM. In a 30-s GNSS denial, the average position error is reduced by 41% compared to inertial navigation and 6.2% compared to LSTM. The model maintains positioning accuracy within 3% of the GNSS/INS output under normal conditions, demonstrating its feasibility and effectiveness. This approach offers a promising solution for autonomous tractor navigation in GNSS-denied agricultural environments, contributing to precision agriculture.https://www.mdpi.com/2032-6653/16/1/11autonomous electric tractorintegrated navigationGNSS signal denialneural network modelextended Kalman filterconvolutional neural network |
| spellingShingle | Yangming Hu Liyou Xu Xianghai Yan Ningjie Chang Qigang Wan Yiwei Wu A Tractor Work Position Prediction Method Based on CNN-BiLSTM Under GNSS Signal Denial World Electric Vehicle Journal autonomous electric tractor integrated navigation GNSS signal denial neural network model extended Kalman filter convolutional neural network |
| title | A Tractor Work Position Prediction Method Based on CNN-BiLSTM Under GNSS Signal Denial |
| title_full | A Tractor Work Position Prediction Method Based on CNN-BiLSTM Under GNSS Signal Denial |
| title_fullStr | A Tractor Work Position Prediction Method Based on CNN-BiLSTM Under GNSS Signal Denial |
| title_full_unstemmed | A Tractor Work Position Prediction Method Based on CNN-BiLSTM Under GNSS Signal Denial |
| title_short | A Tractor Work Position Prediction Method Based on CNN-BiLSTM Under GNSS Signal Denial |
| title_sort | tractor work position prediction method based on cnn bilstm under gnss signal denial |
| topic | autonomous electric tractor integrated navigation GNSS signal denial neural network model extended Kalman filter convolutional neural network |
| url | https://www.mdpi.com/2032-6653/16/1/11 |
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