导航

Acta Aeronautica et Astronautica Sinica ›› 2025, Vol. 46 ›› Issue (10): 31251.doi: 10.7527/S1000-6893.2024.31251

• Reviews • Previous Articles    

Review on multi-source detection technologies for birds and drones in airport clearance area

Weishi CHEN(), Hongchuang NIU, Xin WANG, Jian WAN, Xianfeng LU, Jie ZHANG, Qingbin WANG   

  1. Airport Research Institute,China Academy of Civil Aviation Science and Technology,Beijing 100028,China
  • Received:2024-09-23 Revised:2024-10-14 Accepted:2024-11-22 Online:2024-12-24 Published:2024-11-29
  • Contact: Weishi CHEN E-mail:wishchen@buaa.edu.cn
  • Supported by:
    National Key Research and Development Program of China(2023YFB2604103);National Natural Science Foundation of China(U2433211);Basic Research Project of CAST(x242060302216)

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 a nd 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.

Key words: airport clearance area, flying bird, UAV, multi-source fusion detection, deep learning

CLC Number: