Research for the Positioning Optimization for Portable Field Terrain Mapping Equipment Based on the Adaptive Unscented Kalman Filter Algorithm
Field positioning (FP) is a key technique in the digitalization of agriculture. By integrating sensors and mapping techniques, FP can convey critical information such as soil quality, plant distribution, and topography. Utilizing vehicles for field applications provides precise control and scientifi...
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/22/4248 |
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| author | Jiaxing Xie Zhenbang Yu Gaotian Liang Xianbing Fu Peng Gao Huili Yin Daozong Sun Weixing Wang Yueju Xue Jiyuan Shen Jun Li |
| author_facet | Jiaxing Xie Zhenbang Yu Gaotian Liang Xianbing Fu Peng Gao Huili Yin Daozong Sun Weixing Wang Yueju Xue Jiyuan Shen Jun Li |
| author_sort | Jiaxing Xie |
| collection | DOAJ |
| description | Field positioning (FP) is a key technique in the digitalization of agriculture. By integrating sensors and mapping techniques, FP can convey critical information such as soil quality, plant distribution, and topography. Utilizing vehicles for field applications provides precise control and scientific management for agricultural production. Compared to conventional methods, which often struggle with the complexities of field conditions and suffer from insufficient accuracy, this study employs a novel approach using self-developed multi-sensor array hardware as a portable field topographic surveying device. This innovative setup effectively navigates challenging field conditions to collect raw data. Data fusion is carried out using the Unscented Kalman Filter (UKF) algorithm. Building on this, this study combines the good point set and Opposition-based Differential Evolution for a joint improvement of the Slime Mould Algorithm. This is linked with the UKF algorithm to establish loss value feedback, realizing the adaptive parameter adjustment of the UKF algorithm. This reduces the workload of parameter setting and enhances the precision of data fusion. The improved algorithm optimizes parameters with an efficiency increase of 40.43%. Combining professional, mapping-grade total stations for accuracy comparison, the final test results show an absolute error of less than 0.3857 m, achieving decimeter-level precision in field positioning. This provides a new application technology for better implementation of agricultural digitalization. |
| format | Article |
| id | doaj-art-0fa2d196bdfd4f5d9a92324448d0ccff |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-0fa2d196bdfd4f5d9a92324448d0ccff2025-08-20T01:53:57ZengMDPI AGRemote Sensing2072-42922024-11-011622424810.3390/rs16224248Research for the Positioning Optimization for Portable Field Terrain Mapping Equipment Based on the Adaptive Unscented Kalman Filter AlgorithmJiaxing Xie0Zhenbang Yu1Gaotian Liang2Xianbing Fu3Peng Gao4Huili Yin5Daozong Sun6Weixing Wang7Yueju Xue8Jiyuan Shen9Jun Li10College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaSchool of Electronics and Communication Engineering, Sun Yet-sen University, Guangzhou 510275, ChinaCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaZhujiang College, South China Agricultural University, Guangzhou 510900, ChinaCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaMaoming Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Maoming 525000, ChinaCollege of Engineering, South China Agricultural University, Guangzhou 510642, ChinaField positioning (FP) is a key technique in the digitalization of agriculture. By integrating sensors and mapping techniques, FP can convey critical information such as soil quality, plant distribution, and topography. Utilizing vehicles for field applications provides precise control and scientific management for agricultural production. Compared to conventional methods, which often struggle with the complexities of field conditions and suffer from insufficient accuracy, this study employs a novel approach using self-developed multi-sensor array hardware as a portable field topographic surveying device. This innovative setup effectively navigates challenging field conditions to collect raw data. Data fusion is carried out using the Unscented Kalman Filter (UKF) algorithm. Building on this, this study combines the good point set and Opposition-based Differential Evolution for a joint improvement of the Slime Mould Algorithm. This is linked with the UKF algorithm to establish loss value feedback, realizing the adaptive parameter adjustment of the UKF algorithm. This reduces the workload of parameter setting and enhances the precision of data fusion. The improved algorithm optimizes parameters with an efficiency increase of 40.43%. Combining professional, mapping-grade total stations for accuracy comparison, the final test results show an absolute error of less than 0.3857 m, achieving decimeter-level precision in field positioning. This provides a new application technology for better implementation of agricultural digitalization.https://www.mdpi.com/2072-4292/16/22/4248spatial positioningUnscented Kalman Filtertopographic mapping |
| spellingShingle | Jiaxing Xie Zhenbang Yu Gaotian Liang Xianbing Fu Peng Gao Huili Yin Daozong Sun Weixing Wang Yueju Xue Jiyuan Shen Jun Li Research for the Positioning Optimization for Portable Field Terrain Mapping Equipment Based on the Adaptive Unscented Kalman Filter Algorithm Remote Sensing spatial positioning Unscented Kalman Filter topographic mapping |
| title | Research for the Positioning Optimization for Portable Field Terrain Mapping Equipment Based on the Adaptive Unscented Kalman Filter Algorithm |
| title_full | Research for the Positioning Optimization for Portable Field Terrain Mapping Equipment Based on the Adaptive Unscented Kalman Filter Algorithm |
| title_fullStr | Research for the Positioning Optimization for Portable Field Terrain Mapping Equipment Based on the Adaptive Unscented Kalman Filter Algorithm |
| title_full_unstemmed | Research for the Positioning Optimization for Portable Field Terrain Mapping Equipment Based on the Adaptive Unscented Kalman Filter Algorithm |
| title_short | Research for the Positioning Optimization for Portable Field Terrain Mapping Equipment Based on the Adaptive Unscented Kalman Filter Algorithm |
| title_sort | research for the positioning optimization for portable field terrain mapping equipment based on the adaptive unscented kalman filter algorithm |
| topic | spatial positioning Unscented Kalman Filter topographic mapping |
| url | https://www.mdpi.com/2072-4292/16/22/4248 |
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