The navigation of small unmanned aerial vehicles (UAVs) mainly depends on global positioning systems (GPSs). However, GPSs are vulnerable to attack by spoofing, which causes the UAVs to lose their positioning ability. To address this issue, we propose a deep learning method to detect the spoofing of GPS signals received by small UAVs. Firstly, we describe the GPS signal dataset acquisition and preprocessing methods; these include the hardware system of the UAV and the jammer used in the experiment, the time and weather conditions of the data collection, the use of Spearman correlation coefficients for preprocessing, and the use of SVM-SMOTE to solve the spoofing data imbalance. Next, we introduce a PCA-CNN-LSTM model. We used principal component analysis (PCA) of the model to extract feature information related to spoofing from the GPS signal dataset. The convolutional neural network (CNN) in the model was used to extract local features in the GPS signal dataset, and long short-term memory (LSTM) was used as a posterior module of the CNN for further processing and modeling. To minimize randomness and chance in the simulation experiments, we used the 10-fold cross-validation method to train and evaluate the computational performance of our spoofing machine learning model. We conducted a series of experiments in a numerical simulation environment and evaluated the proposed model against the most advanced traditional machine learning and deep learning models. The results and analysis show that the PCA-CNN-LSTM neural network model achieved the highest accuracy (0.9949). This paper provides a theoretical basis and technical support for spoofing detection for small-UAV GPS signals.

Teleoperation has been widely used to perform tasks in dangerous and unreachable environments by replacing humans with controlled agents. The idea of human-robot interaction (HRI) is very important in teleoperation. Conventional HRI input devices include keyboard, mouse and joystick, etc. However, they are not suitable for handicapped users or people with disabilities. These devices also increase the mental workload of normal users due to simultaneous operation of multiple HRI input devices by hand. Hence, HRI based on gaze tracking with an eye tracker is presented in this study. The selection of objects is of great importance and occurs at a high frequency during HRI control. This paper introduces gaze gestures as an object selection strategy into HRI for drone teleoperation. In order to test and validate the performance of gaze gestures selection strategy, we evaluate objective and subjective measurements, respectively. Drone control performance, including mean task completion time and mean error rate, are the objective measurements. The subjective measurement is the analysis of participant perception. The results showed gaze gestures selection strategy has a great potential as an additional HRI for use in agent teleoperation.

Eye detection plays an important role in many fields, because eyes provide prominent facial feature information. However, changes in face pose, illumination variation, with glasses, and eye occlusions can make it difficult to detect eyes well from fa