Anomaly detection in quadcopter flight: Harnessing frequency domain analysis and barnacle mating optimization
Ensuring the safety and efficiency of unmanned aerial vehicles (UAVs) requires effective fault detection and identification (FDI). Traditional multi-stage FDI methods, particularly those using residual detection layers, increase complexity and computational cost, limiting real-time applications. Thi...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
IAES
2025
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/45310/1/Anomaly%20detection%20in%20quadcopter%20flight-Harnessing%20frequency%20domain%20analysis.pdf http://umpir.ump.edu.my/id/eprint/45310/ https://doi.org/10.11591/eei.v14i4.9224 |
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| Summary: | Ensuring the safety and efficiency of unmanned aerial vehicles (UAVs) requires effective fault detection and identification (FDI). Traditional multi-stage FDI methods, particularly those using residual detection layers, increase complexity and computational cost, limiting real-time applications. This study proposes a single-stage anomaly detection framework integrating barnacle mating optimization (BMO) with discrete cosine transform (DCT) for UAV fault detection. While prior research explored model-based and data-driven FDI, bio-inspired optimization techniques remain underexplored in frequency-domain analysis. This study develops a BMO-based fitness function analyzing 3rd, 5th, and 7th harmonic peaks to detect UAV anomalies. Software-in-the-Loop (SITL) simulations validate the method, achieving a 5-second optimal frame size, mean absolute percentage error (MAPE) of 0.05, and root mean square error (RMSE) of 195.52. The findings confirm that a single-stage detection framework via optimization method and frequency domain analysis is possible, making it viable for real-time UAV applications. This study bridges the gap in bio-inspired UAV fault detection, paving the way for safer and more efficient UAV operations. |
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