Unsupervised Anomaly Detection of Unmanned Aerial Vehicles based on Spatial-Temporal Model with Flight Data
Pengcheng Nan, Guoqian Jiang, Jingchao Zhang, Yingwei Li, Xiaoli Li · 2023
Anomaly detection of Unmanned Aerial Vehicles (UAV) has gained great importance to guarantee safety. In this paper, we propose an unsupervised anomaly detection framework based on spatial-temporal feature learning of available multivariate flight data. First, a spatial-temporal prediction model is designed based on convolutional neural network (CNN) and long short-term memory (LSTM), which can extract the spatial-temporal features from multivariate time series sensor variables in a sequential manner. Then, a low-pass filter is utilized to smooth the residuals to decrease the impact of noise on anomaly detection performance. Finally, anomaly detection is achieved by comparing the smoothed residuals with a statistical threshold. A real flight dataset is used to verify its validation and experimental results demonstrated that our proposed method outperforms several state-of-the-art methods.