基于自生成深度神经网络的4D航迹预测

Self-generated deep neural network based 4D trajectory prediction

针对四维(4D)航迹预测的实时性不强和存在迭代误差的问题,提出了一种自动生成的条件变分自动编码器(AutoCVAE)。它以编码-解码的形式直接对未来一段时间的航迹进行预测,并能灵活选取观测点个数和预测步长。该方法以处理后的广播式自动相关监视(ADS-B)数据为引导,以减小预测误差为目标,通过贝叶斯优化的方法,在预定义的搜索空间内进行模型结构搜索,每一次的超参数取值都会参考之前的评估结果,使得每一次的模型结构都能向目标更靠近一点,最终实现了一个基于ADS-B数据的高精度的4D航迹预测模型。实验得出,所提模型能快速准确地进行航迹的实时预测,其中经纬度平均绝对预测误差(MAE)均小于0.03°,高度MAE小于30 m,各时刻点的时间误差也不会超过10 s,每次批量预测轨迹的延迟时间不超过0.2 s。

Since 4-Dimensional(4 D)trajectory prediction is not real-time and has the iterative error,an Automatically generated Conditional Variational Auto-Encoder(AutoCVAE)was proposed.It is in the form of encoding-decoding to predict the future trajectory directly,and can select observation number and prediction step flexibly.The method was guided by the preprocessed Automatic Dependent Surveillance-Broadcast(ADS-B)data,and with the reduction of the prediction error as the goal.By means of Bayesian optimization,the model structure was searched within the predefined search space.The hyper parameter values of each time were chosen by referencing the previous evaluation results,so that the structure of the new model obtained in each time was able to be closer to the target,and ultimately,a high precision 4 D trajectory prediction model based on ADS-B data was completed.In the experiments,the proposed model was able to predict the trajectory quickly and accurately in real time with the Mean Absolute Error(MAE)of both latitude and longitude less than0.03 degrees,the altitude MAE under 30 m,the time error at each time point not exceeded 10 s,and each batch trajectory prediction delay within 0.2 s.