Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162347
Title: Wind patterns analysis on temporal scales for safe UAV operations using statistical approaches
Authors: Hu, Xinting
Pang, Bizhao
Low, Kin Huat
Keywords: Engineering::Mechanical engineering
Issue Date: 2022
Source: Hu, X., Pang, B. & Low, K. H. (2022). Wind patterns analysis on temporal scales for safe UAV operations using statistical approaches. 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC). https://dx.doi.org/10.1109/DASC55683.2022.9925790
Abstract: The wind is one of the major factors that may cause unmanned aerial vehicles (UAVs) to crash and pose fatality risk to the population and property damage risk to infrastructures. This paper investigates wind patterns on temporal scales to identify high-risk periods in terms of wind conditions for safe UAV operations in urban airspace. The research starts with the historical wind speed data analysis using statistical approaches. As the wind speed data does not follow normal distribution after checking, a nonparametric approach of the Kruskal-Wallis test is applied for hypothesis testing to see if there is a significant difference in the median wind speed in different years. Regression analyses are also performed for monthly wind speed data to check any significant trends that could facilitate the predictions of average wind speed in the long term. This study will contribute to safe air traffic management for UAV operations in low-altitude urban airspace by mitigating adverse wind effects.
URI: https://hdl.handle.net/10356/162347
ISBN: 978-1-6654-8607-1
ISSN: 2155-7209
DOI: 10.1109/DASC55683.2022.9925790
Rights: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/DASC55683.2022.9925790.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Conference Papers
MAE Conference Papers

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