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.

本文提出了一种增强的 CNN-LSTM 网络作为战斗机飞行轨迹预测方法，该方法从空间和时间两个维度提取特征，通过模拟实验证明相比于原始的 CNN-LSTM 方法，预测精度提高了 32% 和 34%。

使用增强型 CNN-LSTM 网络进行飞行轨迹预测

Flight Trajectory Prediction Using an Enhanced CNN-LSTM Network

Ground-air negotiation via speech communication is a vital prerequisite for
ensuring safety and efficiency in air traffic control (ATC) operations.
However, with the increase in traffic flow, incorrect instructions caused by
human factors bring a great threat to ATC safety. Existing flight trajectory
prediction (FTP) approaches primarily rely on the flight status of historical
trajectory, leading to significant delays in the prediction of real-time
maneuvering instruction, which is not conducive to conflict detection. A major
reason is that spoken instructions and flight trajectories are presented in
different modalities in the current air traffic control (ATC) system, bringing
great challenges to considering the maneuvering instruction in the FTP tasks.
In this paper, a spoken instruction-aware FTP framework, called SIA-FTP, is
innovatively proposed to support high-maneuvering FTP tasks by incorporating
instant spoken instruction. To address the modality gap and minimize the data
requirements, a 3-stage learning paradigm is proposed to implement the SIA-FTP
framework in a progressive manner, including trajectory-based FTP pretraining,
intent-oriented instruction embedding learning, and multi-modal finetuning.
Specifically, the FTP model and the instruction embedding with maneuvering
semantics are pre-trained using volumes of well-resourced trajectory and text
data in the 1st and 2nd stages. In succession, a multi-modal fusion strategy is
proposed to incorporate the pre-trained instruction embedding into the FTP
model and integrate the two pre-trained networks into a joint model. Finally,
the joint model is finetuned using the limited trajectory-instruction data to
enhance the FTP performance within maneuvering instruction scenarios. The
experimental results demonstrated that the proposed framework presents an
impressive performance improvement in high-maneuvering scenarios.

该论文提出了一种基于话语指令感知的飞行轨迹预测框架，名为 SIA-FTP，在高机动飞行预测任务中具有重要的应用，通过三阶段的学习模式实现预测，包括基于轨迹的预训练、基于意图的指令嵌入学习和多模式微调，实验结果表明，该框架在高机动情境下实现了出色的性能提升。

SIA-FTP: 语音指令感知的飞行轨迹预测框架

SIA-FTP: A Spoken Instruction Aware Flight Trajectory Prediction  Framework

Flight Trajectory Prediction (FTP) is an essential task in Air Traffic
Control (ATC), which can assist air traffic controllers to manage airspace more
safely and efficiently. Existing approaches generally perform multi-horizon FTP
tasks in an autoregressive manner, which is prone to suffer from error
accumulation and low-efficiency problems. In this paper, a novel framework,
called FlightBERT++, is proposed to i) forecast multi-horizon flight
trajectories directly in a non-autoregressive way, and ii) improved the
limitation of the binary encoding (BE) representation in the FlightBERT
framework. Specifically, the proposed framework is implemented by a generalized
Encoder-Decoder architecture, in which the encoder learns the temporal-spatial
patterns from historical observations and the decoder predicts the flight
status for the future time steps. Compared to conventional architecture, an
extra horizon-aware contexts generator (HACG) is dedicatedly designed to
consider the prior horizon information that enables us to perform multi-horizon
non-autoregressive prediction. Additionally, a differential prediction strategy
is designed by well considering both the stationarity of the differential
sequence and the high-bits errors of the BE representation. Moreover, the
Bit-wise Weighted Binary Cross Entropy loss function is proposed to optimize
the proposed framework that can further constrain the high-bits errors of the
predictions. Finally, the proposed framework is validated on a real-world
flight trajectory dataset. The experimental results show that the proposed
framework outperformed the competitive baselines.

本论文提出了一种名为 FlightBERT++ 的新框架，采用了广义的编码器 - 解码器架构，通过考虑先前时间步长的信息，以及采用比特加权二元交叉熵的丢失函数，以完成多时域非自回归航班轨迹预测任务，并在真实飞行轨迹数据集上完成实验，取得了比其他基本模型更高的预测性能。