Drone detection method based on K-Medoids to extract channel state characteristics
Effective management of low-altitude targets is key to promot the development of the low-altitude economy. In urban environments, strong clutter and building occlusion make it difficult for traditional radar detection methods to effectively monitor low-speed drones. Based on this, a new approach of...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2025-01-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2025008/ |
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| _version_ | 1823864670207868928 |
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| author | SONG Lingyu PAN Peng LIU Tianle |
| author_facet | SONG Lingyu PAN Peng LIU Tianle |
| author_sort | SONG Lingyu |
| collection | DOAJ |
| description | Effective management of low-altitude targets is key to promot the development of the low-altitude economy. In urban environments, strong clutter and building occlusion make it difficult for traditional radar detection methods to effectively monitor low-speed drones. Based on this, a new approach of drone detection was proposed, which involved identifying changes in channel state characteristics to determine whether a drone was presented in a specified area. The core of this method lied in utilizing the already widely deployed mobile base stations and other external radiation sources in cities, capturing the impact of drone presence on the number of multipath channel paths by using the K-Medoids clustering algorithm, to achieve drone perception. This method did not require the construction of an accurate reference signal nor the use of Doppler systems to suppress strong clutter. Simulation results show that the proposed method can achieve detection probabilities of over 80% within a range of 1 square kilometer, and the detection probability can reach about 90% as the range decreases. Therefore, it is capable of effectively detecting low-altitude, slow-moving drones in urban scenarios. |
| format | Article |
| id | doaj-art-37c3b871406a497e88426a2b6ac015c3 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-37c3b871406a497e88426a2b6ac015c32025-02-08T19:00:24ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012025-01-0141758782011875Drone detection method based on K-Medoids to extract channel state characteristicsSONG LingyuPAN PengLIU TianleEffective management of low-altitude targets is key to promot the development of the low-altitude economy. In urban environments, strong clutter and building occlusion make it difficult for traditional radar detection methods to effectively monitor low-speed drones. Based on this, a new approach of drone detection was proposed, which involved identifying changes in channel state characteristics to determine whether a drone was presented in a specified area. The core of this method lied in utilizing the already widely deployed mobile base stations and other external radiation sources in cities, capturing the impact of drone presence on the number of multipath channel paths by using the K-Medoids clustering algorithm, to achieve drone perception. This method did not require the construction of an accurate reference signal nor the use of Doppler systems to suppress strong clutter. Simulation results show that the proposed method can achieve detection probabilities of over 80% within a range of 1 square kilometer, and the detection probability can reach about 90% as the range decreases. Therefore, it is capable of effectively detecting low-altitude, slow-moving drones in urban scenarios.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2025008/dronechannel state informationexternal radiation sourceK-Medoids algorithm |
| spellingShingle | SONG Lingyu PAN Peng LIU Tianle Drone detection method based on K-Medoids to extract channel state characteristics Dianxin kexue drone channel state information external radiation source K-Medoids algorithm |
| title | Drone detection method based on K-Medoids to extract channel state characteristics |
| title_full | Drone detection method based on K-Medoids to extract channel state characteristics |
| title_fullStr | Drone detection method based on K-Medoids to extract channel state characteristics |
| title_full_unstemmed | Drone detection method based on K-Medoids to extract channel state characteristics |
| title_short | Drone detection method based on K-Medoids to extract channel state characteristics |
| title_sort | drone detection method based on k medoids to extract channel state characteristics |
| topic | drone channel state information external radiation source K-Medoids algorithm |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2025008/ |
| work_keys_str_mv | AT songlingyu dronedetectionmethodbasedonkmedoidstoextractchannelstatecharacteristics AT panpeng dronedetectionmethodbasedonkmedoidstoextractchannelstatecharacteristics AT liutianle dronedetectionmethodbasedonkmedoidstoextractchannelstatecharacteristics |