Drone Remote Identification Based on Zadoff-Chu Sequences and Time-Frequency Images
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914261828108288 |
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| author | Li, Jie Li, Jing Lv, Lu Zhang, Peixin Gong, Fengkui |
| author_facet | Li, Jie Li, Jing Lv, Lu Zhang, Peixin Gong, Fengkui |
| contents | We propose an algorithm based on Zadoff-Chu (ZC) sequences and time-frequency images (TFI) to achieve drone remote identification (RID). Specifically, by analyzing the modulation parameters and frame structures of drone ratio-frequency (RF) signals in the DroneRFa dataset, we extract prior information about ZC sequences with surprising correlation properties and robustness. Cross-correlation is performed between locally generated ZC sequences and drone signals to derive ZC sequence-based features. Then, these ZF sequence features are fused with TFI features containing communication protocol information to achieve drone RID. To reduce computational costs, data reduction of the cross-correlation features is performed by analyzing the frame structures and modulation parameters, ensuring that the feature performance remained unaffected. Three feature fusion methods, namely probability-weighted addition, feature vector addition, and feature vector concatenation, are analyzed. Simulation results demonstrate that the proposed algorithm improves the average accuracy by at least 2.5\% compared to existing methods, which also indicate robust RID performance under burst interference and background noise. For RF sampling signals at varying flight distances, the proposed algorithm achieves a maximum accuracy of 99.11\%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02844 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Drone Remote Identification Based on Zadoff-Chu Sequences and Time-Frequency Images Li, Jie Li, Jing Lv, Lu Zhang, Peixin Gong, Fengkui Signal Processing We propose an algorithm based on Zadoff-Chu (ZC) sequences and time-frequency images (TFI) to achieve drone remote identification (RID). Specifically, by analyzing the modulation parameters and frame structures of drone ratio-frequency (RF) signals in the DroneRFa dataset, we extract prior information about ZC sequences with surprising correlation properties and robustness. Cross-correlation is performed between locally generated ZC sequences and drone signals to derive ZC sequence-based features. Then, these ZF sequence features are fused with TFI features containing communication protocol information to achieve drone RID. To reduce computational costs, data reduction of the cross-correlation features is performed by analyzing the frame structures and modulation parameters, ensuring that the feature performance remained unaffected. Three feature fusion methods, namely probability-weighted addition, feature vector addition, and feature vector concatenation, are analyzed. Simulation results demonstrate that the proposed algorithm improves the average accuracy by at least 2.5\% compared to existing methods, which also indicate robust RID performance under burst interference and background noise. For RF sampling signals at varying flight distances, the proposed algorithm achieves a maximum accuracy of 99.11\%. |
| title | Drone Remote Identification Based on Zadoff-Chu Sequences and Time-Frequency Images |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2504.02844 |