Unmanned aerial vehicle assisted localization using multi-sensor fusion and ground vehicle approach
An accurate localization of unmanned aerial vehicles (UAVs) is crucial for the execution of its growing applications such as surveillance and rescue missions. Previous researches have extensively studied the usage of sensor fusion algorithms to combine the sensors on board of the UAV to improve i...
| Main Authors: | , , , |
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| Format: | Article |
| Published: |
Aeronautical and Astronautical Society of the Republic of China
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/102540/ |
| _version_ | 1848863824341368832 |
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| author | Sharif Himmat, Abdelrazig Zhahir, Amzari Md Ali, Syaril Azrad Ahmad, Mohamed Tarmizi |
| author_facet | Sharif Himmat, Abdelrazig Zhahir, Amzari Md Ali, Syaril Azrad Ahmad, Mohamed Tarmizi |
| author_sort | Sharif Himmat, Abdelrazig |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | An accurate localization of unmanned aerial vehicles (UAVs) is crucial
for the execution of its growing applications such as surveillance and rescue
missions. Previous researches have extensively studied the usage of sensor
fusion algorithms to combine the sensors on board of the UAV to improve its
localization. However, application of collaborative localization techniques in
UAV navigation has not been investigated thus far. These novel algorithms
stand to improve the stability and accuracy of UAV localization approaches
through incorporation of additional sensors from other moving targets such
as an unmanned ground vehicle (UGV). It is believed that the accuracy of the
UAV localization will be further improved with help of multi-sensor Kalman
filter (MS-KF) and this collaborative sensor fusion approach leads to a better
accuracy than that of the single-sensor Kalman filter (SS-KF) approach. The
obtained results in this study show promising improvements of both position
and attitude with MS-KF. In comparison, the mean square error (MSE) for
position is 0.005 and 0.026 for the developed MS-KF and SS-KF, respectively.
Meanwhile, MSE for attitude is 2.396e-5 and 8.11e-4 for the developed MS-
KF and SS-KF, respectively. Based on these findings, the positive potential
of collaborative sensor fusion approach has been aptly highlighted. |
| first_indexed | 2025-11-15T13:39:03Z |
| format | Article |
| id | upm-102540 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:39:03Z |
| publishDate | 2022 |
| publisher | Aeronautical and Astronautical Society of the Republic of China |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1025402024-03-21T08:40:27Z http://psasir.upm.edu.my/id/eprint/102540/ Unmanned aerial vehicle assisted localization using multi-sensor fusion and ground vehicle approach Sharif Himmat, Abdelrazig Zhahir, Amzari Md Ali, Syaril Azrad Ahmad, Mohamed Tarmizi An accurate localization of unmanned aerial vehicles (UAVs) is crucial for the execution of its growing applications such as surveillance and rescue missions. Previous researches have extensively studied the usage of sensor fusion algorithms to combine the sensors on board of the UAV to improve its localization. However, application of collaborative localization techniques in UAV navigation has not been investigated thus far. These novel algorithms stand to improve the stability and accuracy of UAV localization approaches through incorporation of additional sensors from other moving targets such as an unmanned ground vehicle (UGV). It is believed that the accuracy of the UAV localization will be further improved with help of multi-sensor Kalman filter (MS-KF) and this collaborative sensor fusion approach leads to a better accuracy than that of the single-sensor Kalman filter (SS-KF) approach. The obtained results in this study show promising improvements of both position and attitude with MS-KF. In comparison, the mean square error (MSE) for position is 0.005 and 0.026 for the developed MS-KF and SS-KF, respectively. Meanwhile, MSE for attitude is 2.396e-5 and 8.11e-4 for the developed MS- KF and SS-KF, respectively. Based on these findings, the positive potential of collaborative sensor fusion approach has been aptly highlighted. Aeronautical and Astronautical Society of the Republic of China 2022 Article PeerReviewed Sharif Himmat, Abdelrazig and Zhahir, Amzari and Md Ali, Syaril Azrad and Ahmad, Mohamed Tarmizi (2022) Unmanned aerial vehicle assisted localization using multi-sensor fusion and ground vehicle approach. Journal of Aeronautics Astronautics and Aviation, 54 (3). 251 - 260. ISSN 1990-7710 https://www.airitilibrary.com/Article/Detail/P20140627004-202209-202204060005-202204060005-251-260 10.6125/JoAAA.202209_54(3).02 |
| spellingShingle | Sharif Himmat, Abdelrazig Zhahir, Amzari Md Ali, Syaril Azrad Ahmad, Mohamed Tarmizi Unmanned aerial vehicle assisted localization using multi-sensor fusion and ground vehicle approach |
| title | Unmanned aerial vehicle assisted localization using multi-sensor fusion and ground vehicle approach |
| title_full | Unmanned aerial vehicle assisted localization using multi-sensor fusion and ground vehicle approach |
| title_fullStr | Unmanned aerial vehicle assisted localization using multi-sensor fusion and ground vehicle approach |
| title_full_unstemmed | Unmanned aerial vehicle assisted localization using multi-sensor fusion and ground vehicle approach |
| title_short | Unmanned aerial vehicle assisted localization using multi-sensor fusion and ground vehicle approach |
| title_sort | unmanned aerial vehicle assisted localization using multi-sensor fusion and ground vehicle approach |
| url | http://psasir.upm.edu.my/id/eprint/102540/ http://psasir.upm.edu.my/id/eprint/102540/ http://psasir.upm.edu.my/id/eprint/102540/ |