Deep neural network for non-cooperative space target intention recognition

Given the critical importance of intention recognition for non-cooperative space targets in enhancing spatial situational awareness and mitigating threats, this paper proposes a fast and accurate method based on a deep neural network for identifying the intentions. The proposed method takes the instantaneous single relative motion position and velocity of the non-cooperative target as input and calculates the probability of different potential intentions in real-time. To this end, the possible geometric configurations of relative motions of the targets are characterized and classified based on the Clohessy-Wiltshire (CW) equation, and intentions with respect to different types of motion are defined and grouped accordingly. To ensure the method's applicability for large range scenario, the required dataset in two-body dynamics is generated using satellite orbit extrapolation techniques. The deep neural network is trained on the dataset using back-propagation, and experiments are designed to evaluate the method's performance and the recognition accuracy reached 0.9289. The proposed method outperforms the dynamical characteristics matching-based method in terms of recognition accuracy, range of applicability, and time efficiency. The method has potential applications in spacecraft collision warning and orbital pursuit-evasion games, where rapid and accurate identification of non-cooperative target intentions is critical.