İnsansız Hava Aracı Telemetrisinde Otomatik Makine Öğrenmesi Tabanlı Anomali Tespiti

Yıl 2025, Cilt: 37 Sayı: 2, 699 - 709, 30.09.2025

Anıl Sezgin , Rasim Keskin , Aytuğ Boyacı

https://doi.org/10.35234/fumbd.1668498

Öz

İnsansız Hava Araçlarının (İHA) telemetri verilerinin güvenilir şekilde analiz edilmesi, özellikle karmaşık ve riskli ortamlarda görev başarısı ve operasyonel güvenlik açısından kritik öneme sahiptir. Bu veri akışları, sensör arızaları, çevresel etkenler veya siber-fiziksel saldırılar nedeniyle anormallikler içerebilir. Bu nedenle, sağlam bir anomali tespit mekanizması gereklidir. Bu çalışma, yüksek frekanslı İHA telemetrisi için özel olarak tasarlanmış, gözetimsiz bir anomali tespit çerçevesi sunmaktadır. Yaklaşım, alan bilgisine dayalı özellik mühendisliğini, model seçimi ve hiperparametre ayarlarını otomatikleştiren bir AutoML tabanlı optimizasyon süreciyle birleştirir. Sistem; Local Outlier Factor, Isolation Forest, One-Class SVM ve Elliptic Envelope olmak üzere dört farklı gözetimsiz algoritmayı entegre ederek, İHA operasyonlarının dinamik doğasına uyum sağlar. 48 farklı İHA görevinden toplanan 127.000 örnek içeren gerçek dünya veri kümesi üzerinde yapılan değerlendirmelerde, uzmanlar tarafından etiketlenmiş anomali segmentleri yalnızca doğrulama amacıyla kullanılmıştır. En iyi performans, %92 doğruluk, %88 kesinlik, %85 duyarlılık ve %86 F1-skoru ile Local Outlier Factor algoritması tarafından elde edilmiştir. Gerçek zamanlı uygulamalar için ölçeklenebilir ve düşük gecikmeli olarak tasarlanan bu sistem, İHA’larda otomatik arıza izleme ve güvenli, dayanıklı hava araçları ekosistemlerinin gelişimine önemli katkılar sunmaktadır.

Anahtar Kelimeler

Anomali tespiti , insansız hava araçları , otomatik makine öğrenmesi

Anomaly Detection in Unmanned Aerial Vehicle Telemetry Using Automated Machine Learning

Öz

Reliable analysis of UAV telemetry data is critical for mission safety, especially as drones are increasingly deployed in complex and high-risk environments. These data streams often include anomalies arising from sensor faults, environmental disruptions, or cyber-physical attacks, making robust anomaly detection essential. This study introduces an unsupervised anomaly detection framework designed specifically for high-frequency UAV telemetry. It combines domain-driven feature engineering with an AutoML-based optimization pipeline that enables automated model selection and hyperparameter tuning. The framework integrates four unsupervised algorithms—Local Outlier Factor, Isolation Forest, One-Class SVM, and Elliptic Envelope—ensuring adaptability to the dynamic nature of UAV operations. Evaluated on a real-world dataset of 127,000 samples from 48 UAV missions, the system uses expert-labeled anomaly segments solely for validation to preserve the integrity of unsupervised learning. Among all methods, Local Outlier Factor yielded the best results with 0.920 accuracy, 0.880 precision, 0.850 recall, and 0.860 F1-score. Scalable and low-latency, the proposed solution is well-suited for real-time deployment. By bridging theoretical advances with operational needs, this work contributes to safer and more resilient aerial robotic systems.

Anahtar Kelimeler

Anomaly detection , unmanned aerial vehicles , automated machine learning

Kaynakça

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