Drone Remote Identification Based on Zadoff-Chu Sequences and Time-Frequency Images

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 radio-frequency (RF) signals, we extract prior information about ZC sequences with remarkable correlation properties and robustness. Cross-correlation is performed between locally generated ZC sequences and drone signals to derive ZC sequence-based features. Then, these ZC 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 that are independent of model architectures, namely probability-weighted addition, feature vector addition, and feature vector concatenation, are analyzed. Simulation results demonstrate that the proposed algorithm improves the RID average evaluation metrics by at least 2.27% compared to existing algorithms, which have a RID latency of 0.3172 s. The proposed algorithm also exhibits strong robustness under varying flight distances, diverse visual environments, different background noise levels, and multi-drone coexistence scenarios.