A Long-term Projectile Impact Point Prediction Correction Method Based on LSTM Network

Aiming at the large deviation problem in impact point prediction during the rising period of fire locating radar, a novel long-term projectile impact point prediction correction method based on long short-term memory (LSTM) network is proposed. Due to the small sample problem, a dual-drive method combining knowledge-driven and data-driven method is exploited, with unscented Kalman filter predictions as prior knowledge and the LSTM network to learn the longitudinal and lateral deviations and then correct the impact point of extrapolated trajectory. Simulations show that the accuracy of long-term impact point prediction is improved significantly with a small number of training samples. The longitudinal prediction accuracy is increased by 55%, and the lateral accuracy is increased by 85% to 91%.