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.
Highlights
The trajectory prediction (TP), as a core technique in air traffic studies, has been attracting more and more attention from all over the world
To learn a probabilistic distribution to achieve the conflict detection task. It is a type of deep neural networks (DNNs) organized with the hierarchical architecture, in which Gaussian process (GP) serve as the nonlinear activation between two neurons
Unlike the Altitude-Driven Flight Trajectory Prediction (A-FTP), a deep Gaussian process (DGP) model cannot be optimized from flight positions directly since the speed instructions can be performed at any time and location
Summary
The trajectory prediction (TP), as a core technique in air traffic studies, has been attracting more and more attention from all over the world. Learning from existing state-of-the-art approaches, a deep Gaussian process (DGP)-based framework is proposed to achieve the trajectory prediction, and further to implement a probabilistic based short-term conflict detection (STCD). Based on the proposed DGP trajectory prediction approach, a probabilistic is implemented pair-wisely, in which the Monte Carlo method is applied to simplify the computation conflict detection approach is implemented pair-wisely, in which the Monte Carlo method is applied by sampling flight positions from the predicted DGP models. The main proposed trajectory prediction models and conflict detection algorithm are validated on real contributions of this work can be summarized as follows: operating data. A probabilistic conflict detection algorithm is implemented based on the proposed DGP trajectory prediction approach. The Gaussian distribution provides required probabilistic elements of trajectory positions and further supports the conflict detection, in which the Monte Carlo sampling is applied to simplify the integral solution
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