An Aircraft Trajectory Generation Method Based on Deep Hybrid Density Network

Abstract

The invention discloses an aircraft trajectory generation method based on a deep mixed density network. First, read aircraft trajectory data, and perform quality analysis and preprocessing on the data; then, construct a deep mixed density network model, the structure of which is from above The bottom is the input layer, the bidirectional long-term short-term memory network layer, the mixed density model network layer, and the output layer; and the hyperparameters and weight parameters of the network model are set and optimized; finally, a large number of aircraft trajectories are processed through the deep mixed density neural network. Modeling is used to obtain the statistical characteristics of aircraft trajectories, and the roulette sampling method is used to sample aircraft trajectory data to generate trajectories. The present invention can obtain the space-time characteristics and operation rules of aircraft trajectory, construct aircraft trajectory generation model, realize trajectory simulation and trajectory prediction functions, so as to help aircraft plan collision avoidance exercises and help conduct off-line performance and safety analysis, thereby improving the efficiency of air transportation Efficiency, security and predictability.

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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