Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine

Unmanned Aerial Vehicles (UAVs) are widely used and meet many demands in military civilian fields. With the continuous enrichment extensive expansion of application scenarios, safety UAVs is constantly being challenged. To address this challenge, we propose algorithms to detect anomalous data collected from drones improve drone safety. We deployed a one-class kernel extreme learning machine (OCKELM) anomalies data. By default, OCKELM uses radial basis (RBF) function as model. performance OCKELM, choose Triangular Global Alignment Kernel (TGAK) instead an RBF introduce Fast Independent Component Analysis (FastICA) algorithm reconstruct UAV Based on above improvements, create novel anomaly detection strategy FastICA-TGAK-OCELM. The method finally validated UCI dataset detected Aeronautical Laboratory Failures Anomalies (ALFA) dataset. experimental results show that compared with other methods, accuracy improved by more than 30%, point effectively detected.