Unmanned Aerial Vehicle Flight Data Anomaly Detection and Recovery Prediction Based on Spatio-Temporal Correlation

Abstract

With the development of unmanned aerial vehicle (UAV) technology, a UAV is gradually applied to a variety of civil fields, such as photography, power line inspection, and environmental monitoring. At the same time, the safety and reliability of a UAV also attract wide attention. Anomaly detection is one of the key technologies to improve the safety of an UAV. The structure of the UAV system is complex, and there are complex spatio-temporal correlations among the high-dimensional flight data with many parameters. However, the existing methods often ignore the spatio-temporal correlation of data and lack parameter selection, which is used to abandon the parameters without a positive impact on anomaly detection results. This article proposes a spatio-temporal correlation based long short-term memory (LSTM) method for anomaly detection and recovery prediction of UAV flight data. First, an artificial neural network correlation analysis is proposed to preliminarily mine the spatio-temporal correlation in flight data and to obtain the correlation parameter sets. Second, the LSTM model is established, and the mapping among different parameters is realized. Finally, anomaly detection and recovery prediction are carried out based on parameter sets mapping model. The effectiveness of the proposed method is verified by generating sample sets with anomaly injection on real UAV flight data.