Low-Cost Sensors State Estimation Algorithm for a Small Hand-Launched Solar-Powered UAV
In order to reduce the cost of the flight controller and improve the control accuracy of solar-powered unmanned aerial vehicle (UAV), three state estimation algorithms based on the extended Kalman filter (EKF) with different structures are proposed: Three-stage series, full-state direct and indirect...
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| Format: | Article |
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MDPI AG
2019-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/21/4627 |
| _version_ | 1811187287035215872 |
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| author | An Guo Zhou Zhou Xiaoping Zhu Fan Bai |
| author_facet | An Guo Zhou Zhou Xiaoping Zhu Fan Bai |
| author_sort | An Guo |
| collection | DOAJ |
| description | In order to reduce the cost of the flight controller and improve the control accuracy of solar-powered unmanned aerial vehicle (UAV), three state estimation algorithms based on the extended Kalman filter (EKF) with different structures are proposed: Three-stage series, full-state direct and indirect state estimation algorithms. A small hand-launched solar-powered UAV without ailerons is used as the object with which to compare the algorithm structure, estimation accuracy, and platform requirements and application. The three-stage estimation algorithm has a position accuracy of 6 m and is suitable for low-cost small, low control precision UAVs. The precision of full-state direct algorithm is 3.4 m, which is suitable for platforms with low-cost and high-trajectory tracking accuracy. The precision of the full-state indirect method is similar to the direct, but it is more stable for state switching, overall parameters estimation, and can be applied to large platforms. A full-scaled electric hand-launched UAV loaded with the three-stage series algorithm was used for the field test. Results verified the feasibility of the estimation algorithm and it obtained a position estimation accuracy of 23 m. |
| first_indexed | 2024-04-11T13:59:44Z |
| format | Article |
| id | doaj.art-dd99bd43c11f43cca8ec68da398176c0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:59:44Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-dd99bd43c11f43cca8ec68da398176c02022-12-22T04:20:09ZengMDPI AGSensors1424-82202019-10-011921462710.3390/s19214627s19214627Low-Cost Sensors State Estimation Algorithm for a Small Hand-Launched Solar-Powered UAVAn Guo0Zhou Zhou1Xiaoping Zhu2Fan Bai3School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaScience and Technology on UAV Laboratory, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, ChinaIn order to reduce the cost of the flight controller and improve the control accuracy of solar-powered unmanned aerial vehicle (UAV), three state estimation algorithms based on the extended Kalman filter (EKF) with different structures are proposed: Three-stage series, full-state direct and indirect state estimation algorithms. A small hand-launched solar-powered UAV without ailerons is used as the object with which to compare the algorithm structure, estimation accuracy, and platform requirements and application. The three-stage estimation algorithm has a position accuracy of 6 m and is suitable for low-cost small, low control precision UAVs. The precision of full-state direct algorithm is 3.4 m, which is suitable for platforms with low-cost and high-trajectory tracking accuracy. The precision of the full-state indirect method is similar to the direct, but it is more stable for state switching, overall parameters estimation, and can be applied to large platforms. A full-scaled electric hand-launched UAV loaded with the three-stage series algorithm was used for the field test. Results verified the feasibility of the estimation algorithm and it obtained a position estimation accuracy of 23 m.https://www.mdpi.com/1424-8220/19/21/4627low-cost sensorstate estimationextended kalman filter (ekf)three-stage seriesfull-state directfull-state indirectmodel calibration |
| spellingShingle | An Guo Zhou Zhou Xiaoping Zhu Fan Bai Low-Cost Sensors State Estimation Algorithm for a Small Hand-Launched Solar-Powered UAV Sensors low-cost sensor state estimation extended kalman filter (ekf) three-stage series full-state direct full-state indirect model calibration |
| title | Low-Cost Sensors State Estimation Algorithm for a Small Hand-Launched Solar-Powered UAV |
| title_full | Low-Cost Sensors State Estimation Algorithm for a Small Hand-Launched Solar-Powered UAV |
| title_fullStr | Low-Cost Sensors State Estimation Algorithm for a Small Hand-Launched Solar-Powered UAV |
| title_full_unstemmed | Low-Cost Sensors State Estimation Algorithm for a Small Hand-Launched Solar-Powered UAV |
| title_short | Low-Cost Sensors State Estimation Algorithm for a Small Hand-Launched Solar-Powered UAV |
| title_sort | low cost sensors state estimation algorithm for a small hand launched solar powered uav |
| topic | low-cost sensor state estimation extended kalman filter (ekf) three-stage series full-state direct full-state indirect model calibration |
| url | https://www.mdpi.com/1424-8220/19/21/4627 |
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