Dim and Small Target Detection for Drone Broadcast Frames Based on Time-Frequency Analysis

• URL: http://arxiv.org/abs/2505.18167v2
• Date: Sun, 22 Jun 2025 03:00:03 GMT
• Title: Dim and Small Target Detection for Drone Broadcast Frames Based on Time-Frequency Analysis
• Authors: Jie Li, Jing Li, Zhanyu Ju, Fengkui Gong, Lu Lv,
• Abstract summary: We propose a dim and small target detection algorithm for drone broadcast frames based on the time-frequency analysis of communication protocol.<n>The proposed algorithm improves the evaluation metrics by 2.27% compared to existing algorithms.
• Score: 13.693769465573297
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a dim and small target detection algorithm for drone broadcast frames based on the time-frequency analysis of communication protocol. Specifically, by analyzing modulation parameters and frame structures, the prior knowledge of transmission frequency, signal bandwidth, Zadoff-Chu (ZC) sequences, and frame length of drone broadcast frames is established. The RF signals are processed through the designed filter banks, and the frequency domain parameters of bounding boxes generated by the detector are corrected with transmission frequency and signal bandwidth. Given the remarkable correlation characteristics of ZC sequences, the frequency domain parameters of bounding boxes with low confidence scores are corrected based on ZC sequences and frame length, which improves the detection accuracy of dim targets under low signal-to noise ratio situations. Besides, a segmented energy refinement method is applied to mitigate the deviation caused by interference signals with high energy strength, which ulteriorly corrects the time domain detection parameters for dim targets. As the sampling duration increases, the detection speed improves while the detection accuracy of broadcast frames termed as small targets decreases. The trade-off between detection accuracy and speed versus sampling duration is established, which helps to meet different drone regulation requirements. Simulation results demonstrate that the proposed algorithm improves the evaluation metrics by 2.27\% compared to existing algorithms. The proposed algorithm also performs strong robustness under varying flight distances, diverse types of environment noise, and different flight visual environment. Besides, the broadcast frame decoding results indicate that 97.30\% accuracy of RID has been achieved.

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