Spatial Non-cooperative Target Behavior Intent Recognition Based on Data Generation and Deep Neural Networks

Under the conditions of informatization, the space environment has become increasingly complex, and the number of non cooperative targets in space is growing. Ground operators find it difficult to quickly and accurately identify the intentions of non cooperative targets based on their motion patterns. Therefore, a spatial non cooperative target behavior intention recognition model based on Stacked Autoencoder (SAE) and Gated Recurrent network Unit (GRU) was proposed to assist ground operators in identifying the intention of non cooperative targets. This model utilizes an autoencoder to compress time series data, extract key features, and uses a GRU network to classify trajectories. At present, there is no publicly available orbit data for non cooperative target behavior, and it is difficult to fully train the model with only a small amount of known data. To solve the problem of poor recognition performance caused by insufficient samples, a simulation sample generation method is proposed, which obtains a large amount of target behavior trajectory data through simulation for the recognition of spatial non cooperative target behavior intentions. After the simulation data is obtained, the simulation data set is used as the input. The experimental results show that compared with the single model only using the Long and Short Term Memory network (LSTM), GRU-FCN, SAE, and Back-Propagation (BP), this method has significantly improved the accuracy and loss value performance indicators, reaching 97.8% accuracy.