Flight Trajectory Prediction Using an Enhanced CNN-LSTM Network

TL;DR

This paper introduces an enhanced CNN-LSTM network that combines spatial, temporal, and attention mechanisms to improve the accuracy of fighter flight trajectory prediction in complex air combat scenarios.

Contribution

The paper presents a novel CNN-LSTM model with social pooling and attention mechanisms specifically designed for fighter trajectory prediction, outperforming previous methods.

Findings

Improved prediction accuracy with 32% and 34% enhancements in ADE and FDE.

Effective integration of spatial, temporal, and attention features.

Validated through extensive simulation experiments.

Abstract

Aiming at the problem of low accuracy of flight trajectory prediction caused by the high speed of fighters, the diversity of tactical maneuvers, and the transient nature of situational change in close range air combat, this paper proposes an enhanced CNN-LSTM network as a fighter flight trajectory prediction method. Firstly, we extract spatial features from fighter trajectory data using CNN, aggregate spatial features of multiple fighters using the social-pooling module to capture geographic information and positional relationships in the trajectories, and use the attention mechanism to capture mutated trajectory features in air combat; subsequently, we extract temporal features by using the memory nature of LSTM to capture long-term temporal dependence in the trajectories; and finally, we merge the temporal and spatial features to predict the flight trajectories of enemy fighters. Extensive simulation experiments verify that the proposed method improves the trajectory prediction accuracy compared to the original CNN-LSTM method, with the improvements of 32% and 34% in ADE and FDE indicators.