Research And Application Of Air Target Intent Recognition Technology Based On Machine Learning

The previous research on air target intention recognition is insufficient,and the traditional method of air target intention recognition only relying on expert experience can no longer satisfy the increasingly complex situational environment information.Obtaining effective data from a large amount of uncertain environmental information,further extract valuable characteristic information,and complete the intention recognition of current and future aerial targets has become a hot issue in current research.In the traditional intent recognition algorithm,the intent of the target is usually inferred based on the feature information of a single moment,which leads to the lack of the dynamic attributes and temporal change characteristics of the target during the process of intent recognition,which affects the accuracy of intent recognition.In order to solve this problem,neural network is used as a technical means to excavate intrinsic feature associations from target state sequences at multiple times in a row,and complete target intent recognition by fitting the mapping relationship between samples and labels.Following are the contributions in this thesis.1.The problem of air target intent recognition is studied.Aiming at the problem that long-term dependence is difficult to obtain in the problem of aerial target intent recognition,a cross-attention mechanism based on temporal attention mechanism and channel attention is adopted,and the TCNCCA-GRU model is built on this basis.Test experiments are carried out on 5 comparative models using the dataset constructed from the simulation system,illustrating the superiority of the proposed model in terms of recognition accuracy.At the same time,ablation experiments are conducted,and tests are completed on public datasets and simulated datasets,which demonstrate the effectiveness of the proposed cross-attention mechanism.2.The problem of air target trajectory prediction is studied.Aiming at the problem that the current air target trajectory prediction technology does not pay enough attention to the interactive features between targets,a layered graph attention mechanism is proposed,and the HGATCVAE model is built based on this.Using the public data set of aircraft trajectories collected over the airport,the test experiments were carried out on five comparison models,and the results showed that they achieved better results in both the actual distance error and the final distance error.3.The air target intent recognition system is designed and implemented.The system includes a scene simulation module and a core function module.The scene simulation module is used to simulate a real scene to obtain a simulation dataset.The core function module includes two parts: aircraft target intent recognition and aircraft trajectory prediction.The front-end framework is used to complete the development,realizing an intent recognition system that is rich in visualization and easy to operate.