An End-to-End LSTM-MDN Network for Projectile Trajectory Prediction

Trajectory prediction from radar data is an example of a signal processing problem, which is challenging due to small sample sizes, high noise, non-stationarity, and non-linear. A data-driven LSTM-MDN end-to-end network from incomplete and noisy radar measurements to predict projectile trajectory is investigated in this paper. Traditional prediction algorithm usually uses Kalman Filter (KF) or the like to estimate target’s position and speed, then uses the numerical integral, such as Runge-Kutta, Adams, etc. to extrapolate the launch point or impact point, which mainly relies on the accuracy of dynamic models. A Long Short-Term Memory (LSTM) network is designed to estimate the real position from sampled and noisy radar measurements series, and a Mixture Density Network (MDN) is developed for trajectory extrapolation and projectile launch point prediction. These two subnetworks are integrated into an end-to-end network, which is trained by the radar measurement samples of a projectile and the corresponding ground truth of its launch point. Compared with the traditional methods, amount of experiments show that our proposed method is superior to the traditional model-based methods, and its adaptability to the range of initial launch angle is significantly better than the traditional method.