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

arXiv: https://arxiv.org/abs/2505.18167 · HTML: https://mlsanity.com/paper/2505.18167/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 51 references that are in the map.
- First posted on arXiv 14 May 2025.

## Builds on (most cited first, 15 of 15)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Infrared Small Target Detection with Scale and Location Sensitivity](https://mlsanity.com/paper/2403.19366/) (2024)
- [Joint Radar and Communication Design: Applications, State-of-the-art, and the Road Ahead](https://mlsanity.com/paper/1906.00789/) (2019)
- [Triple-domain Feature Learning with Frequency-aware Memory Enhancement for Moving Infrared Small Target Detection](https://mlsanity.com/paper/2406.06949/) (2024)
- [CDFormer:When Degradation Prediction Embraces Diffusion Model for Blind Image Super-Resolution](https://mlsanity.com/paper/2405.07648/) (2024)
- [SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Network](https://mlsanity.com/paper/2211.16785/) (2022)
- [Safeguarding Next Generation Multiple Access Using Physical Layer Security Techniques: A Tutorial](https://mlsanity.com/paper/2403.16477/) (2024)
- [Collaborative Sensing in Perceptive Mobile Networks: Opportunities and Challenges](https://mlsanity.com/paper/2205.15805/) (2022)
- [Joint Signal Detection and Automatic Modulation Classification via Deep Learning](https://mlsanity.com/paper/2405.00736/) (2024)
- [Robust Low-Cost Drone Detection and Classification in Low SNR Environments](https://mlsanity.com/paper/2406.18624/) (2024)
- [Drone Remote Identification Based on Zadoff-Chu Sequences and Time-Frequency Images](https://mlsanity.com/paper/2504.02844/) (2025)
- [DJI drone IDs are not encrypted](https://mlsanity.com/paper/2207.10795/) (2022)

## Abstract (arXiv)

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.