Trajectory prediction of flying vehicles based on deep learning methods

To deal with the threat from flying vehicles, it is of great significance to accurately predict the flight trajectory of flying vehicles using the known historical position data. In this paper, we investigate eight typical types of maneuvers of flying vehicles. Then, according to the kinematics model of the flying vehicle, eight kinds of typical maneuver trajectory equations are introduced. Next, the data set for neural network training is created by varying the critical parameters of the trajectory equation. Accordingly, we train the offline Long-Short Term Memory (LSTM) using 1/12 of the dataset of trajectories, and results show that the trained network can classify types of trajectories accurately. Based on the results of classification using the offline LSTM, we propose two trajectory prediction methods, and both of them achieve excellent prediction with and without noise interference. Furthermore, we propose an online prediction method, and it can accurately predict the subsequent trajectory of flying vehicles when the type of trajectory is not clear. And we propose a filter to improve the accuracy of the online prediction method for trajectory prediction with noise. The simulation results verify that all three previously proposed methods can accurately predict the next move based on historical data.

Automated summary

This paper investigates typical maneuvers of flying vehicles and introduces corresponding trajectory equations based on the kinematics model. Multiple accurate prediction methods for flying vehicle trajectory are proposed through neural network training and simulation verification.