Development of a New Airport Unusual-Weather Detection System with Aircraft Surveillance Information

Shau-Shiun Jan, Ya Tzu Chen

研究成果: Article

摘要

This paper proposes a new aviation unusual-weather detection system constructed to augment existing aviation unusual-weather alert systems. Due to a lack of ground meteorological stations with sufficient ground altitude near airports, existing aviation unusual-weather alert systems can only detect unusual-weather conditions near the ground surface. Thus, this paper uses the automatic dependent surveillance - broadcast (ADS-B) signal transmitted by commercial aircraft to acquire vertical weather information for low-level weather conditions. Specifically, we propose an aviation unusual-weather detection model to establish the system using both the aircraft irregular-movement detection algorithm and the machine learning method. The performance of the proposed unusual-weather detection model is validated with actual ADS-B signals from several flights collected at the airport. The experiment results show the accuracy rates of aviation normal/unusual-weather classification above 96% and false positive rates below 1% for decent flight phases.

原文English
文章編號8754800
頁(從 - 到)9543-9551
頁數9
期刊IEEE Sensors Journal
19
發行號20
DOIs
出版狀態Published - 2019 十月 15

指紋

airports
surveillance
Airports
weather
Aviation
aircraft
Aircraft
aeronautics
Learning systems
flight
commercial aircraft
weather stations
machine learning
ground stations
Experiments

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Electrical and Electronic Engineering

引用此文

@article{8ea1343e68ab4c6eb2f9174da904c8b9,
title = "Development of a New Airport Unusual-Weather Detection System with Aircraft Surveillance Information",
abstract = "This paper proposes a new aviation unusual-weather detection system constructed to augment existing aviation unusual-weather alert systems. Due to a lack of ground meteorological stations with sufficient ground altitude near airports, existing aviation unusual-weather alert systems can only detect unusual-weather conditions near the ground surface. Thus, this paper uses the automatic dependent surveillance - broadcast (ADS-B) signal transmitted by commercial aircraft to acquire vertical weather information for low-level weather conditions. Specifically, we propose an aviation unusual-weather detection model to establish the system using both the aircraft irregular-movement detection algorithm and the machine learning method. The performance of the proposed unusual-weather detection model is validated with actual ADS-B signals from several flights collected at the airport. The experiment results show the accuracy rates of aviation normal/unusual-weather classification above 96{\%} and false positive rates below 1{\%} for decent flight phases.",
author = "Shau-Shiun Jan and Chen, {Ya Tzu}",
year = "2019",
month = "10",
day = "15",
doi = "10.1109/JSEN.2019.2926391",
language = "English",
volume = "19",
pages = "9543--9551",
journal = "IEEE Sensors Journal",
issn = "1530-437X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "20",

}

Development of a New Airport Unusual-Weather Detection System with Aircraft Surveillance Information. / Jan, Shau-Shiun; Chen, Ya Tzu.

於: IEEE Sensors Journal, 卷 19, 編號 20, 8754800, 15.10.2019, p. 9543-9551.

研究成果: Article

TY - JOUR

T1 - Development of a New Airport Unusual-Weather Detection System with Aircraft Surveillance Information

AU - Jan, Shau-Shiun

AU - Chen, Ya Tzu

PY - 2019/10/15

Y1 - 2019/10/15

N2 - This paper proposes a new aviation unusual-weather detection system constructed to augment existing aviation unusual-weather alert systems. Due to a lack of ground meteorological stations with sufficient ground altitude near airports, existing aviation unusual-weather alert systems can only detect unusual-weather conditions near the ground surface. Thus, this paper uses the automatic dependent surveillance - broadcast (ADS-B) signal transmitted by commercial aircraft to acquire vertical weather information for low-level weather conditions. Specifically, we propose an aviation unusual-weather detection model to establish the system using both the aircraft irregular-movement detection algorithm and the machine learning method. The performance of the proposed unusual-weather detection model is validated with actual ADS-B signals from several flights collected at the airport. The experiment results show the accuracy rates of aviation normal/unusual-weather classification above 96% and false positive rates below 1% for decent flight phases.

AB - This paper proposes a new aviation unusual-weather detection system constructed to augment existing aviation unusual-weather alert systems. Due to a lack of ground meteorological stations with sufficient ground altitude near airports, existing aviation unusual-weather alert systems can only detect unusual-weather conditions near the ground surface. Thus, this paper uses the automatic dependent surveillance - broadcast (ADS-B) signal transmitted by commercial aircraft to acquire vertical weather information for low-level weather conditions. Specifically, we propose an aviation unusual-weather detection model to establish the system using both the aircraft irregular-movement detection algorithm and the machine learning method. The performance of the proposed unusual-weather detection model is validated with actual ADS-B signals from several flights collected at the airport. The experiment results show the accuracy rates of aviation normal/unusual-weather classification above 96% and false positive rates below 1% for decent flight phases.

UR - http://www.scopus.com/inward/record.url?scp=85072543452&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85072543452&partnerID=8YFLogxK

U2 - 10.1109/JSEN.2019.2926391

DO - 10.1109/JSEN.2019.2926391

M3 - Article

AN - SCOPUS:85072543452

VL - 19

SP - 9543

EP - 9551

JO - IEEE Sensors Journal

JF - IEEE Sensors Journal

SN - 1530-437X

IS - 20

M1 - 8754800

ER -