Vision-Based Moving-Target Geolocation Using Dual Unmanned Aerial Vehicles
This paper develops a framework for geolocating ground-based moving targets with images taken from dual unmanned aerial vehicles (UAVs). Unlike the usual moving-target geolocation methods that rely heavily on accurate navigation state sensors or assumptions of the known target’s altitude, the propos...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/2/389 |
| _version_ | 1797437517594624000 |
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| author | Tingwei Pan Jianjun Gui Hongbin Dong Baosong Deng Bingxu Zhao |
| author_facet | Tingwei Pan Jianjun Gui Hongbin Dong Baosong Deng Bingxu Zhao |
| author_sort | Tingwei Pan |
| collection | DOAJ |
| description | This paper develops a framework for geolocating ground-based moving targets with images taken from dual unmanned aerial vehicles (UAVs). Unlike the usual moving-target geolocation methods that rely heavily on accurate navigation state sensors or assumptions of the known target’s altitude, the proposed framework does not have the same limitations and performs geolocation of moving targets utilizing dual UAVs equipped with the low-quality navigation state sensors. Considering the Gaussian measurement errors and yaw-angle measurement bias provided by low-quality sensors, we first propose an epipolar constraint-based corresponding-point-matching method, which enables the historical measurement data to be used to estimate the current position of the moving target; after that, we propose a target altitude estimation method based on multiview geometry, which utilizes multiple images, including historical images, to estimate the altitude of the moving target; finally, considering the negative influence of yaw-angle measurement bias on the processes of target altitude estimation and parameter regression, we take advantage of multiple iterations among the two processes to accurately estimate the moving target’s two-dimensional position and the yaw-angle measurement biases of two UAVs. The effectiveness and practicability of the framework proposed in this paper are proved by simulation experiments and actual flight experiments. |
| first_indexed | 2024-03-09T11:20:29Z |
| format | Article |
| id | doaj.art-ac7ca3dd83194a42a6d18da7a373b493 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T11:20:29Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-ac7ca3dd83194a42a6d18da7a373b4932023-12-01T00:19:59ZengMDPI AGRemote Sensing2072-42922023-01-0115238910.3390/rs15020389Vision-Based Moving-Target Geolocation Using Dual Unmanned Aerial VehiclesTingwei Pan0Jianjun Gui1Hongbin Dong2Baosong Deng3Bingxu Zhao4Department of Computer Science and Technology, Harbin Engineering University, Harbin 150009, ChinaDefense Innovation Institute, Chinese Academy of Military Science, Beijing 100071, ChinaDepartment of Computer Science and Technology, Harbin Engineering University, Harbin 150009, ChinaDefense Innovation Institute, Chinese Academy of Military Science, Beijing 100071, ChinaDepartment of Computer Science and Technology, Harbin Engineering University, Harbin 150009, ChinaThis paper develops a framework for geolocating ground-based moving targets with images taken from dual unmanned aerial vehicles (UAVs). Unlike the usual moving-target geolocation methods that rely heavily on accurate navigation state sensors or assumptions of the known target’s altitude, the proposed framework does not have the same limitations and performs geolocation of moving targets utilizing dual UAVs equipped with the low-quality navigation state sensors. Considering the Gaussian measurement errors and yaw-angle measurement bias provided by low-quality sensors, we first propose an epipolar constraint-based corresponding-point-matching method, which enables the historical measurement data to be used to estimate the current position of the moving target; after that, we propose a target altitude estimation method based on multiview geometry, which utilizes multiple images, including historical images, to estimate the altitude of the moving target; finally, considering the negative influence of yaw-angle measurement bias on the processes of target altitude estimation and parameter regression, we take advantage of multiple iterations among the two processes to accurately estimate the moving target’s two-dimensional position and the yaw-angle measurement biases of two UAVs. The effectiveness and practicability of the framework proposed in this paper are proved by simulation experiments and actual flight experiments.https://www.mdpi.com/2072-4292/15/2/389altitude estimationcomputer visioncorresponding point matchingmoving target geolocationunmanned aerial vehicle (UAV) |
| spellingShingle | Tingwei Pan Jianjun Gui Hongbin Dong Baosong Deng Bingxu Zhao Vision-Based Moving-Target Geolocation Using Dual Unmanned Aerial Vehicles Remote Sensing altitude estimation computer vision corresponding point matching moving target geolocation unmanned aerial vehicle (UAV) |
| title | Vision-Based Moving-Target Geolocation Using Dual Unmanned Aerial Vehicles |
| title_full | Vision-Based Moving-Target Geolocation Using Dual Unmanned Aerial Vehicles |
| title_fullStr | Vision-Based Moving-Target Geolocation Using Dual Unmanned Aerial Vehicles |
| title_full_unstemmed | Vision-Based Moving-Target Geolocation Using Dual Unmanned Aerial Vehicles |
| title_short | Vision-Based Moving-Target Geolocation Using Dual Unmanned Aerial Vehicles |
| title_sort | vision based moving target geolocation using dual unmanned aerial vehicles |
| topic | altitude estimation computer vision corresponding point matching moving target geolocation unmanned aerial vehicle (UAV) |
| url | https://www.mdpi.com/2072-4292/15/2/389 |
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