Mitigating Sensor Quality Issues in UAV System Identification Through Wavelet Decomposition - Jimenez-Soler, Pedro; Lichota, Piotr (Bulletin of the Polish Academy of Sciences Technical Sciences, 2025)
Unmanned aerial vehicles (UAVs) require precise system identification for optimal performance and safety, yet sensor noise and signal distortion frequently compromise data quality. Recent studies have explored various approaches to mitigate these issues. However, this study introduces a novel method that utilizes wavelet transform techniques, distinctively enhancing UAV sensor signal processing. Unlike conventional methods that primarily focus on noise reduction, this approach employs multi-resolution wavelet decomposition to denoise and align signals effectively, crucial for accurate system identification. This systematic exploration of various wavelet bases and the application of the output error method for correlating signals provide a unique combination not extensively covered in current literature. The technique was validated using simulated sensor data at 50 Hz from a small UAV platform, the Multiplex®Fun Cub, specifically targeting longitudinal dynamics response. Results demonstrated substantial improvements in signal quality, with significantly enhanced correlation coefficients, showcasing the potential of our wavelet techniques to refine UAV system analysis. This paper presents a comprehensive framework for applying wavelet-based techniques in UAV system identification, significantly advancing the robustness and reliability of identification processes and distinguishing our work from existing methods by its integration of wavelet decomposition and advanced system identification techniques.