A Deep Gaussian Process-Based Flight Trajectory Prediction Approach and Its Application on Conflict Detection

Abstract

In this work, a deep Gaussian process (DGP) based framework is proposed to improve the accuracy of predicting flight trajectory in air traffic research, which is further applied to implement a probabilistic conflict detection algorithm. The Gaussian distribution is applied to serve as the probabilistic representation for illustrating the transition patterns of the flight trajectory, based on which a stochastic process is generated to build the temporal correlations among flight positions, i.e., Gaussian process (GP). Furthermore, to deal with the flight maneuverability of performing controller’s instructions, a hierarchical neural network architecture is proposed to improve the modeling representation for nonlinear features. Thanks to the intrinsic mechanism of the GP regression, the DGP model has the ability of predicting both the deterministic nominal flight trajectory (NFT) and its confidence interval (CI), denoting by the mean and standard deviation of the prediction sequence, respectively. The CI subjects to a Gaussian distribution, which lays the data foundation of the probabilistic conflict detection. Experimental results on real data show that the proposed trajectory prediction approach achieves higher prediction accuracy compared to other baselines. Moreover, the conflict detection approach is also validated by a obtaining lower false alarm and more prewarning time.

Aircraft 4D Trajectory Prediction in Civil Aviation: A Review

“…In addition to the two commonly used methods of regression model and neural network, other machine learning methods have also appeared [73][74][75][76][77][78][79], such as genetic algorithm (GA), ant colony algorithm, and support vector machine (SVM), etc. Here, this is regarded as a separate category.…”

Aircraft Trajectory Prediction With Enriched Intent Using Encoder-Decoder Architecture

“…Such approach, i.e., trajectory clustering combined with multiple predictive models, were also employed for trajectory prediction in [20]- [23]. Other recent studies on the employment of different machine learning algorithms for trajectory prediction include Bayesian deep neural networks [24], [25], variational inference [26], conditional generative adversarial network [27], deep Gaussian process [28]. In addition, a hybrid machine learning-physics approach was recently proposed [29], in which an estimation algorithm (Residual-Mean Interacting Multiple Model) was introduced to improve a machine learning models by accounting for the motion of the aircraft.…”

Flight trajectory prediction enabled by time-frequency wavelet transform

“…With the continual development of the global economy, the air transportation demand has significantly increased across various industries, leading to a surge in flight traffic and airspace complexity. To optimize flight scheduling and improve operational efficiency, the traffic prediction is extensively studied to support air traffic management (ATM), including flight delay prediction 1 , 2 , fuel consumption prediction 3 , 4 , and flight trajectory prediction (FTP) 5 , 6 . Thanks to the supportive ability to the future trajectory-based operation (TBO), the FTP task is attracting increasing research attention for both the academic and industrial fields all over the world, including the Single European Sky ATM Research (SESAR) 7 and the Next Generation Air Transportation System (NextGen) 8 .…”