| Autor: |
Pasquale Longobardi, Aman Sharma, Jan Skaloud |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 12, Pp 91649-91663 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3421579 |
| Popis: |
This paper presents a comparative analysis of two methodologies for estimating unknown parameters in a Vehicle Dynamic Model (VDM)-based sensor fusion framework for small drones. Focusing on a delta-wing drone, we conduct open-air wind tunnel experiments to determine a functional aerodynamic model. Subsequently, we compare two methodologies for unknown model parameters identification, one based on linear regression on wind tunnel experimental data, and the other employing partial-update-based estimators on recorded flight data. The performance of both parameter estimation approaches is then evaluated in a VDM-based framework through three independent test flights. Our results highlight the necessity of wind tunnel experiments for aerodynamic model formulation, while the data-driven method proves useful to identify the parameters at a low cost. Furthermore, we demonstrate that both (flight) data-driven and wind-tunnel experiment-based identified aerodynamics significantly enhance positioning accuracy, particularly in the absence of satellite signals, when integrated with low-cost consumer-grade MEMS inertial sensors. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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