# Fighter flight trajectory prediction based on spatio-temporal graphcial attention network

arXiv: https://arxiv.org/abs/2405.08034 · HTML: https://mlsanity.com/paper/2405.08034/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 13 May 2024.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting](https://mlsanity.com/paper/1907.00235/) (2019)
- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [Conditional Generative Neural System for Probabilistic Trajectory Prediction](https://mlsanity.com/paper/1905.01631/) (2019)
- [Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs](https://mlsanity.com/paper/1912.01118/) (2019)
- [SCALE-Net: Scalable Vehicle Trajectory Prediction Network under Random Number of Interacting Vehicles via Edge-enhanced Graph Convolutional Neural Network](https://mlsanity.com/paper/2002.12609/) (2020)
- [AVGCN: Trajectory Prediction using Graph Convolutional Networks Guided by Human Attention](https://mlsanity.com/paper/2101.05682/) (2021)
- [Flight Trajectory Prediction Using an Enhanced CNN-LSTM Network](https://mlsanity.com/paper/2404.19218/) (2024)

## Abstract (arXiv)

Quickly and accurately predicting the flight trajectory of a blue army fighter in close-range air combat helps a red army fighter gain a dominant situation, which is the winning factor in later air combat. However,due to the high speed and even hypersonic capabilities of advanced fighters, the diversity of tactical maneuvers,and the instantaneous nature of situational transitions,it is difficult to meet the requirements of practical combat applications in terms of prediction accuracy.To improve prediction accuracy,this paper proposes a spatio-temporal graph attention network (ST-GAT) using encoding and decoding structures to predict the flight trajectory. The encoder adopts a parallel structure of Transformer and GAT branches embedded with the multi-head self-attention mechanism in each front end. The Transformer branch network is used to extract the temporal characteristics of historical trajectories and capture the impact of the fighter's historical state on future trajectories, while the GAT branch network is used to extract spatial features in historical trajectories and capture potential spatial correlations between fighters.Then we concatenate the outputs of the two branches into a new feature vector and input it into a decoder composed of a fully connected network to predict the future position coordinates of the blue army fighter.The computer simulation results show that the proposed network significantly improves the prediction accuracy of flight trajectories compared to the enhanced CNN-LSTM network (ECNN-LSTM), with improvements of 47% and 34% in both ADE and FDE indicators,providing strong support for subsequent autonomous combat missions.