Abstract
In recent years, the unmanned aerial vehicle (UAV) has emerged as a significant risk factor in the field of low-altitude safety. Radar technology plays a crucial role in the detection of UAV within low-altitude airspace. During radar detection, avian targets, particularly migratory birds, share similarities with UAV in terms of flight characteristics, such as flight altitude, flight speed, course stability, radar cross-section, making it very difficult to distinguish UAV target from bird targets. In this paper, the flight mechanisms of UAV and migratory birds are analyzed, and a spatial migration intensity perception algorithm based on the trajectory is proposed. The target flight characteristics and spatial correlation characteristics are extracted from recent and historical radar trajectory data. The results of spatial migration intensity perception are combined with various trajectory characteristics, and five machine learning algorithms are used to realize the accurate recognition of UAV targets and birds. Finally, the effectiveness of the algorithm is verified by experimental data.
Original language | English |
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Pages (from-to) | 1055-1062 |
Number of pages | 8 |
Journal | IET Conference Proceedings |
Volume | 2023 |
Issue number | 47 |
DOIs | |
Publication status | Published - 2023 |
Event | IET International Radar Conference 2023, IRC 2023 - Chongqing, China Duration: 3 Dec 2023 → 5 Dec 2023 |
Keywords
- feature extraction
- machine learning
- migration birds
- Radar target recognition