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

arXiv: https://arxiv.org/abs/2504.02844 · HTML: https://mlsanity.com/paper/2504.02844/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2025.

## Built on by (most cited first, 2 of 2)

- [Dim and Small Target Detection for Drone Broadcast Frames Based on Time-Frequency Analysis](https://mlsanity.com/paper/2505.18167/) (2025)
- [Cognitive Fusion of ZC Sequences and Time-Frequency Images for Out-of-Distribution Detection of Drone Signals](https://mlsanity.com/paper/2601.18326/) (2026)

## Abstract (arXiv)

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%.