期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 22, 期 7, 页码 4531-4540出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2020.3023189
关键词
Intrusion detection; light detection and ranging; machine learning; obstacle recognition; unmanned aerial vehicle
资金
- Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia [227]
This study focuses on the UAV network architecture under a common scenario and proposes an obstacle recognition and intrusion detection algorithm based on airborne LiDAR. By preprocessing LiDAR data, generating LiDAR data graphs, clustering intrusions, and completing motion recognition and location detection, the proposed algorithm effectively identifies the moving state of intrusions according to experimental results.
With the rapid development of wireless communication and flight control technologies, the unmanned aerial vehicles (UAVs) have been widely used in multiple application scenarios. A typical scenario is massive crowd management of the multi-millions annual Hajj Pilgrimage to Mecca where UAVs are widely utilized to conduct crowd monitoring by carrying sensory devices. The safe flight of a UAV is crucial for ensuring the successful execution of missions. With the aim to overcome the disadvantage caused by the ground station intrusion detection, the combination of UAV and airborne LiDAR has been widely studied in the field of UAV obstacle recognition. This article studies the UAV network architecture under a common scenario and proposes an obstacle recognition and intrusion detection algorithm for UAV based on an airborne LiDAR (ALORID). First, the preprocessing of the data obtained by a LiDAR, i.e., the coordinate conversion of LiDAR data in combination with UAV motion parameters, is completed. Then, the LiDAR data graph at the current moment is generated by the image noisy point filtering algorithm. After that, the improved density-based spatial clustering of applications with noise (DBSCAN) algorithm is used for image clustering of intrusions to obtain the LiDAR time-domain cumulative graph in a certain detection time. Finally, the motion recognition and location detection of each cluster are completed. The experiment results verify the effectiveness of the proposed algorithm in identifying the moving state of the intrusions.
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