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

TL;DR

This paper introduces a novel time-frequency analysis-based algorithm for detecting dim and small drone broadcast frames, enhancing detection accuracy and robustness under low SNR conditions by leveraging modulation parameters, ZC sequences, and energy refinement.

Contribution

The paper presents a new detection algorithm that combines time-frequency analysis, prior knowledge of transmission parameters, and ZC sequence correlation to improve small target detection in drone communication signals.

Findings

Detection accuracy improved by 2.27% over existing methods

Achieved 97.30% accuracy in broadcast frame decoding

Robust detection under varying noise and flight conditions

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.