A Space Vector–Based Long-Range AOA Localization Algorithm With Reference Points
In long-range missions based on angle-of-arrival positioning, the angle measurement error of unmanned aerial vehicles is a major source of error. Therefore, reducing the unmanned aerial vehicle angle measurement error is crucial to achieve accurate remote positioning. In this paper, we propose a spa...
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/2914212 |
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| author | Chenxin Wang Wenxing Fu Tong Zhang Guangyu Yang |
| author_facet | Chenxin Wang Wenxing Fu Tong Zhang Guangyu Yang |
| author_sort | Chenxin Wang |
| collection | DOAJ |
| description | In long-range missions based on angle-of-arrival positioning, the angle measurement error of unmanned aerial vehicles is a major source of error. Therefore, reducing the unmanned aerial vehicle angle measurement error is crucial to achieve accurate remote positioning. In this paper, we propose a space vector–based method to correct the space vector of the target for the unmanned aerial vehicles when there are fewer than three available reference points, which in turn corrects the angular value of the target relative to the unmanned aerial vehicles. Simulation results show that when the distance between the reference point and the unmanned aerial vehicles is smaller than the distance between the target and the unmanned aerial vehicles, the azimuth measurement error can be reduced to 55% of the original error for the case of a single reference point, while the pitch angle measurement error remains almost unchanged. In the case of more than two reference points, the azimuth measurement error can be reduced to 1e5 and the pitch angle measurement error can be reduced to 30% of the original error. This method can be adapted to the rapid positioning task for high-speed and high-mobility targets without iteration, low computation, good correction effect, and the need of prior known data set reference. |
| format | Article |
| id | doaj-art-f3f63c9aa6674fd491f028c7962363d9 |
| institution | Kabale University |
| issn | 1687-5974 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-f3f63c9aa6674fd491f028c7962363d92025-02-02T23:15:16ZengWileyInternational Journal of Aerospace Engineering1687-59742024-01-01202410.1155/2024/2914212A Space Vector–Based Long-Range AOA Localization Algorithm With Reference PointsChenxin Wang0Wenxing Fu1Tong Zhang2Guangyu Yang3Unmanned System Research InstituteUnmanned System Research InstituteUnmanned System Research InstituteSchool of Marine Science and TechnologyIn long-range missions based on angle-of-arrival positioning, the angle measurement error of unmanned aerial vehicles is a major source of error. Therefore, reducing the unmanned aerial vehicle angle measurement error is crucial to achieve accurate remote positioning. In this paper, we propose a space vector–based method to correct the space vector of the target for the unmanned aerial vehicles when there are fewer than three available reference points, which in turn corrects the angular value of the target relative to the unmanned aerial vehicles. Simulation results show that when the distance between the reference point and the unmanned aerial vehicles is smaller than the distance between the target and the unmanned aerial vehicles, the azimuth measurement error can be reduced to 55% of the original error for the case of a single reference point, while the pitch angle measurement error remains almost unchanged. In the case of more than two reference points, the azimuth measurement error can be reduced to 1e5 and the pitch angle measurement error can be reduced to 30% of the original error. This method can be adapted to the rapid positioning task for high-speed and high-mobility targets without iteration, low computation, good correction effect, and the need of prior known data set reference.http://dx.doi.org/10.1155/2024/2914212 |
| spellingShingle | Chenxin Wang Wenxing Fu Tong Zhang Guangyu Yang A Space Vector–Based Long-Range AOA Localization Algorithm With Reference Points International Journal of Aerospace Engineering |
| title | A Space Vector–Based Long-Range AOA Localization Algorithm With Reference Points |
| title_full | A Space Vector–Based Long-Range AOA Localization Algorithm With Reference Points |
| title_fullStr | A Space Vector–Based Long-Range AOA Localization Algorithm With Reference Points |
| title_full_unstemmed | A Space Vector–Based Long-Range AOA Localization Algorithm With Reference Points |
| title_short | A Space Vector–Based Long-Range AOA Localization Algorithm With Reference Points |
| title_sort | space vector based long range aoa localization algorithm with reference points |
| url | http://dx.doi.org/10.1155/2024/2914212 |
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