Research on Target Trajectory Prediction Based on Kalman Filter and Long Short Term Memory Network

A new prediction algorithm that combines machine learning and classical filtering techniques is proposed to accurately predict the trajectory of airborne targets. This algorithm utilizes Long Short Term Memory (LSTM) networks to learn the motion patterns of targets from historical trajectory data, and combines Kalman Filter (KF) algorithm to dynamically correct the current state, in order to achieve high-precision target trajectory prediction. The results show that the algorithm exhibits high accuracy and adaptability on both simulated and real data.