The efficiency of Unmanned Aerial Vehicles (UAVs) in precision agriculture significantly depends on their design and functional adaptability. This paper presents a focused study on the lateral-directional dynamics of the HyVprop UAV, featuring a distinctive V-tail inverted configuration, tailored for advanced crop imaging tasks. Building prior wavelet transform-based methodologies that addressed sensor quality issues, this research adapts these techniques to the unique aerodynamic characteristics and challenges posed by the HyVprop’s design. The research uses multi-level wavelet decomposition to reduce sensor noise and synchronize lateral-directional signals accurately. This process is essential for precise system identification, which plays a key role in the UAV’s navigation and operational effectiveness within agricultural settings. Enhanced by the Output Error Method, which is refined to improve signal correlation specifically for the V-tail configuration, this approach is tested with simulated sensor data. The findings demonstrate significant improvements in signal quality and correlation coefficients, establishing a comprehensive framework for UAV system identification that enhances reliability and precision in crop monitoring. The paper not only confirms the methodology’s effectiveness but also highlights the specific advantages of adapting system identification techniques to unconventional UAV designs like the V-tail inverted HyVprop.
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