Aircraft trajectory prediction using lstm neural network with embedded convolutional layer

Yutian Pang, Nan Xu, Yongming Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The development of convective weather avoidance algorithm is crucial for aviation operations and it is also a key objective of the next generation air traffic management system. This paper proposes a novel network architecture that embeds convolutional layers into long short-time memory (LSTM) cells to predict the trajectory, based on the convective weather condition along with the flight plan before the aircraft takeoff. The data used in the experiments are history flight track data, the last on-file flight plan, and the time-dependent convective weather map. The history flight data are taken from the NASA Sherlock database and the weather data used in this paper is the Echo Top (ET) convective weather product from Corridor IntegratedWeather System (CIWS). The experiment is conducted using three months history data over the period from Nov 1, 2018 through Feb 5, 2019 with the flights from John F. Kennedy International Airport (JFK) to Los Angeles International Airport (LAX) but the methodology can be applied to the flights between any arbitrary two airports. Interpolation is performed on flight plans and real history tracks to fix the fold number of LSTM cells and also reduce computation complexity. The training loss is defined as the standard Mean Squared Error (MSE) of the predicted tracks and the real history tracks. Adam optimizer is used for backpropagation. Learning from the real historical flight data, the outof- sample test shows that 47.0% of the predicted flight tracks can reduce the deviation compared to the last on-file flight plan. The overall variance is reduced by 12.3%.

Original languageEnglish (US)
Title of host publicationProceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
EditorsN. Scott Clements, Bin Zhang, Abhinav Saxena
PublisherPrognostics and Health Management Society
Edition1
ISBN (Electronic)9781936263059
DOIs
StatePublished - Sep 23 2019
Event11th Annual Conference of the Prognostics and Health Management Society, PHM 2019 - Scottsdale, United States
Duration: Sep 23 2019Sep 26 2019

Publication series

NameProceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
Number1
Volume11
ISSN (Print)2325-0178

Conference

Conference11th Annual Conference of the Prognostics and Health Management Society, PHM 2019
CountryUnited States
CityScottsdale
Period9/23/199/26/19

ASJC Scopus subject areas

  • Information Systems
  • Electrical and Electronic Engineering
  • Health Information Management
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Aircraft trajectory prediction using lstm neural network with embedded convolutional layer'. Together they form a unique fingerprint.

  • Cite this

    Pang, Y., Xu, N., & Liu, Y. (2019). Aircraft trajectory prediction using lstm neural network with embedded convolutional layer. In N. S. Clements, B. Zhang, & A. Saxena (Eds.), Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM (1 ed.). (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM; Vol. 11, No. 1). Prognostics and Health Management Society. https://doi.org/10.36001/phmconf.2019.v11i1.849