A structured sparse subspace learning algorithm for anomaly detection in UAV flight data

Health status monitoring of flight-critical sensors is crucial to the flight safety of unmanned aerial vehicles (UAVs). While many flight data anomaly detection algorithms have been proposed, most do not consider data source information and cannot identify which data sources contribute most to the anomaly, hindering proper fault mitigation. To address this challenge, a structured sparse subspace learning (SSL) anomaly detection (SSSLAD) algorithm, which reformulates anomaly detection as a structured SSL problem, is proposed. A structured norm is imposed on the projection coefficients matrix to achieve structured sparsity and help identify anomaly sources. Utilizing an efficient optimization method based on Nesterov's method and a subspace tracking approach considering temporal dependence, the computation is efficient. Experiments on real UAV flight data sets illustrate that the proposed SSSLAD algorithm can accurately and quickly detect and identify anomalous sources in flight data, outperforming state of art algorithms, both in terms of accuracy and speed.