Abstract:

Trajectory prediction based on observed data is critical to the modern army's precision strike capability. Yet, the existing trajectory prediction methods have suffer from low accuracy and poor real-time performance. Thus, this study proposes a new model called Context-enhanced Long Short-Term Memory(CE-LSTM) Network to make an accurate long-term prediction of the exterior trajectory of incoming projectiles. The proposed method inherits the LSTM network's advantage in that it can approximate any nonlinear function and it has a long-term memory. Furthermore, we create a mixture output unit of the hidden layer to extract short-term context information and approximate the motion states of the incoming projectiles more accurately. The CE-LSTM network is trained with a large-scale dataset consisting of exterior trajectories under different initial conditions to obtain the optimal hyper-parameters. The experimental results show that compared with the methods like external ballistic differential equations and the Gaussian mixture model, the CE-LSTM network performs significantly better in prediction accuracy, and its prediction speed increases by three to ten times. Moreover, the proposed method is highly generalizable.

Key words: trajectory prediction, context-enhanced, long short-term memory network, ballistic differential equations, gaussian mixture model