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Robust trajectory prediction using random forest methodology application to UAS-S4 ehécatl

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Hashemi, Seyed Mohammad, Botez, Ruxandra Mihaela and Ghazi, Georges. 2024. « Robust trajectory prediction using random forest methodology application to UAS-S4 ehécatl ». Aerospace, vol. 11, nº 1.

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Abstract

Accurate aircraft trajectory prediction is fundamental for enhancing air traffic control systems, ensuring a safe and efficient aviation transportation environment. This research presents a detailed study on the efficacy of the Random Forest (RF) methodology for predicting aircraft trajectories. The study compares the RF approach with two established data-driven models, specifically Long Short-Term Memory (LSTM) and Logistic Regression (LR). The investigation utilizes a significant dataset comprising aircraft trajectory time history data, obtained from a UAS-S4 simulator. Experimental results indicate that within a short-term prediction horizon, the RF methodology surpasses both LSTM and LR in trajectory prediction accuracy and also its robustness to overfitting. The research further fine-tunes the performance of the RF methodology by optimizing various hyperparameters, including the number of estimators, features, depth, split, and leaf. Consequently, these results underscore the viability of the RF methodology as a proven alternative to LSTM and LR models for short-term aircraft trajectory prediction.

Item Type: Peer reviewed article published in a journal
Professor:
Professor
Botez, Ruxandra
Ghazi, Georges
Affiliation: Génie des systèmes, Génie des systèmes
Date Deposited: 14 Feb 2024 19:04
Last Modified: 11 Mar 2024 15:52
URI: https://espace2.etsmtl.ca/id/eprint/28344

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