TY - JOUR
T1 - Development of a New Airport Unusual-Weather Detection System with Aircraft Surveillance Information
AU - Jan, Shau Shiun
AU - Chen, Ya Tzu
N1 - Funding Information:
Manuscript received May 29, 2019; accepted June 28, 2019. Date of publication July 3, 2019; date of current version September 18, 2019. This work was supported by the Ministry of Science and Technology, Taiwan, under Grant 102-2221-E-006-079-MY3. The associate editor coordinating the review of this paper and approving it for publication was Dr. Sanket Goel. (Corresponding author: Shau-Shiun Jan.) The authors are with the Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan 70101, Taiwan (e-mail: [email protected]; [email protected]). Digital Object Identifier 10.1109/JSEN.2019.2926391
Publisher Copyright:
© 2001-2012 IEEE.
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.
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U2 - 10.1109/JSEN.2019.2926391
DO - 10.1109/JSEN.2019.2926391
M3 - Article
AN - SCOPUS:85072543452
SN - 1530-437X
VL - 19
SP - 9543
EP - 9551
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 20
M1 - 8754800
ER -