@article{CHEN2025, 
author = {Weishi CHEN and Hongchuang NIU and Xin WANG and Jian WAN and Xianfeng LU and Jie ZHANG and Qingbin WANG},
title = {Review on multi-source detection technologies for birds and drones in airport clearance area},
year = {2025},
journal = {Acta Aeronautica et Astronautica Sinica},
volume = {46},
number = {10},
keywords = {airport clearance area, flying bird, UAV, multi-source fusion detection, deep learning},
url = {https://www.sciopen.com/article/10.7527/S1000-6893.2024.31251},
doi = {10.7527/S1000-6893.2024.31251},
abstract = {Flying birds and drones are the two major sources of hazards threatening the airport clearance area. The mainstream detection technologies for the targets of flying birds and non-cooperative Unmanned Aerial Vehicles (UAVs) are reviewed, such as detection via radar, optoelectronics, radio, and acoustics, and the latest research progress on the typical systems and target recognition and classification algorithms at home and abroad is also discussed. In terms of target classification models, deep learning networks have emerged as a highly regarded research direction. Based on the analysis of the characteristics of various sensor data, a multi-source fusion detection scheme for birds and drones in the airport clearance area is proposed, which is mainly based on radar data, and is supplemented by several other types of data. Multi-source fusion detection and recognition of flying birds and UAV targets is achieved through hierarchical fusion on data level, feature level, and decision level.}
}