Bo Liu

The maneuvering characteristics and range of motion of real aircraft are highly uncertain, which significantly increases the difficulty of trajectory prediction. To solve the problem that high-speed maneuvers and excessive trajectories in airspace cause a decrease in prediction accuracy and to find out the laws of motion hidden in a large number of real trajectories, this paper proposes a deep learning algorithm based on trajectory clustering and spatiotemporal feature extraction, which aims to better describe the regularity of aircraft movement for higher prediction accuracy. First, the abnormal trajectories in the public dataset of automatic dependent surveillance–broadcast (ADS-B) were analyzed, and to ensure the uniform sampling of trajectory data, the cleaning and interpolation of the trajectory data were performed. Then, the Hausdorff distance was used to measure the similarity between the trajectories, K-Medoids was used for clustering, and the corresponding prediction model was established according to the clustering results. Finally, a trajectory spatiotemporal feature extraction network was constructed based on a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network, and a joint attention mechanism was used to obtain the important features of the trajectory points. A large number of actual trajectory prediction experiments showed that the proposed method is more accurate than existing algorithms based on BP, LSTM, and CNN–LSTM models.

The current deep learning (DL)-based Specific Emitter Identification (SEI) methods rely heavily on the training of massive labeled data during the training process. However, the lack of labeled data in a real application would lead to a decrease in the method’s identification performance. In this paper, we propose a self-supervised method via contrast learning (SSCL), which is used to extract fingerprint features from unlabeled data. The proposed method uses large amounts of unlabeled data to constitute positive and negative pairs by designing a composition of data augmentation operations for emitter signals. Then, the pairs would be input into the neural network (NN) for feature extraction, and a contrastive loss function is introduced to drive the network to measure the similarity among data. Finally, the identification model can be completed by fixing the parameters of the feature extraction network and fine-tuning with few labeled data. The simulation experiment result shows that, after being fine-tuned, the proposed method can effectively extract fingerprint features. When the SNR is 20 dB, the identification accuracy reaches 94.45%, which is better than the current mainstream DL approaches.