空战过程中对目标飞行器轨迹的精准预测，能够大幅提升我机取得空中优势的能力。为此，针对现有的各类轨迹预测算法求解出的基准预测值，提出了一种通用性强、计算耗时短、精度提升高的基于Transformer模型的预测误差补偿（TFPEC）方法，以进一步提高轨迹预测结果的准确性。首先，在基准预测值的基础上，建立了基于Transformer的特征提取模型来处理目标飞行器的历史轨迹序列，得到轨迹特征矩阵；其次，利用多层感知机结构对特征矩阵进行回归计算，求解轨迹预测误差补偿值；然后，利用傅里叶变换将轨迹变换到频域进行模值分析，计算误差补偿系数；最后，对基准预测值、误差补偿值以及补偿系数进行综合计算，以获得最终的轨迹预测结果。为验证所提方法的有效性，结合了4种现有轨迹预测算法，在飞行员一对一格斗空战中所产生的数据集上进行了测试。仿真结果表明所提方法能够有效地提升轨迹预测结果的精度，适用于空战环境下的飞行器轨迹预测任务。

Accurate prediction results of the target aircraft trajectory can greatly improve the ability of our aircraft to gain air superiority in the process of air combat. This paper proposes a Transformer-based Prediction Error Compensation （TFPEC） method to further improve the accuracy of the trajectory prediction results based on the benchmarks generated by mature trajectory prediction algorithms. Firstly， a Transformer-based feature extraction model is established to process the historical trajectory sequence of the target aircraft， so as to obtain the trajectory feature matrix. Secondly， the multi-layer perceptron structure is used to perform regression calculation of the feature matrix， so as to calculate the trajectory prediction error compensation value. Thirdly， the Fourier transform is used to transform the trajectory into the frequency domain for modulo analysis， and to calculate the error compensation coefficient. Finally， the benchmark， the error compensation value， and the compensation coefficient are comprehensively calculated to obtain the final prediction result. The proposed method is tested on the one-on-one data set generated by pilots. The simulation results show that compared with the four existing trajectory prediction algorithms， the proposed method can effectively improve the accuracy of trajectory prediction results， and is applicable for aircraft trajectory prediction tasks in the air combat environment.