Aircraft trajectory generation method based on deep hybrid density network

Abstract

The invention discloses an aircraft trajectory generation method based on a deep hybrid density network. The method comprises the steps: firstly reading aircraft trajectory data, and carrying out thequality analysis and preprocessing of the data; then, establishing a deep hybrid density network model, wherein the structure of the model comprises an input layer, a bidirectional long-short-term memory network layer, a hybrid density model network layer and an output layer from top to bottom; setting and optimizing hyper-parameters and weight parameters of the network model; and finally, modeling a large number of aircraft trajectories through a deep hybrid density neural network to obtain statistical characteristics of the aircraft trajectories, and sampling aircraft trajectory data by adopting a roulette disk sampling method to generate trajectories. According to the method, the space-time characteristic and the operation rule of the aircraft trajectory can be obtained, the aircraft trajectory generation model is constructed, and trajectory analogue simulation and trajectory prediction functions are realized, so that the aircraft is helped to plan collision avoidance exercises andhelp to carry out offline performance and safety analysis, and the efficiency, the safety and the predictability of air transportation are improved.

Description

technical field [0001] The invention belongs to the technical field of civil aviation, and in particular relates to an aircraft trajectory generation method based on a deep mixed density network. Background technique [0002] In recent years, with the continuous and rapid development of the air transport industry, the contradiction between limited airspace resources and increasing air traffic flow has deepened day by day, which has intensified potential conflicts between aircraft, increased the load of controllers, and frequent problems such as airspace congestion and flight delays. To this end, countries around the world are gradually changing airspace management and developing a new generation of air traffic management systems in order to solve the problems faced in air traffic management through management and technological innovation, and improve the efficiency and safety of air traffic transportation. [0003] This patent focuses on aircraft trajectory generation techno...

AI Technical Summary

Problems solved by technology

The data-driven method depends on the completeness of large data samples of aircraft trajectories and the ability of the model to represent a large amount of historical trajectory data. However, the existing data-driven trajectory generation methods all use a shallow network structure. Limited

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