Yan Xiang, Han Bing, Su Zhigang, Hao Jingtang
Sino-European Institute of Aviation Engineering, Civil Aviation University of China, Tianjin 300300, China.
Sensors (Basel). 2023 Nov 10;23(22):9098. doi: 10.3390/s23229098.
With the growing integration of drones into various civilian applications, the demand for effective automatic drone identification (ADI) technology has become essential to monitor malicious drone flights and mitigate potential threats. While numerous convolutional neural network (CNN)-based methods have been proposed for ADI tasks, the inherent local connectivity of the convolution operator in CNN models severely constrains RF signal identification performance. In this paper, we propose an innovative hybrid transformer model featuring a CNN-based tokenization method that is capable of generating T-F tokens enriched with significant local context information, and complemented by an efficient gated self-attention mechanism to capture global time/frequency correlations among these T-F tokens. Furthermore, we underscore the substantial impact of incorporating phase information into the input of the SignalFormer model. We evaluated the proposed method on two public datasets under Gaussian white noise and co-frequency signal interference conditions, The SignalFormer model achieved impressive identification accuracy of 97.57% and 98.03% for coarse-grained identification tasks, and 97.48% and 98.16% for fine-grained identification tasks. Furthermore, we introduced a class-incremental learning evaluation to demonstrate SignalFormer's competence in handling previously unseen categories of drone signals. The above results collectively demonstrate that the proposed method is a promising solution for supporting the ADI task in reliable ways.
随着无人机越来越多地融入各种民用应用，对有效的自动无人机识别（ADI）技术的需求对于监控恶意无人机飞行和减轻潜在威胁至关重要。虽然已经提出了许多基于卷积神经网络（CNN）的方法来处理ADI任务，但CNN模型中卷积算子固有的局部连接性严重限制了射频信号识别性能。在本文中，我们提出了一种创新的混合变压器模型，该模型具有基于CNN的分词方法，能够生成富含重要局部上下文信息的时频（T-F）令牌，并辅以高效的门控自注意力机制，以捕捉这些T-F令牌之间的全局时间/频率相关性。此外，我们强调了将相位信息纳入SignalFormer模型输入的重大影响。我们在高斯白噪声和同频信号干扰条件下的两个公共数据集上评估了所提出的方法，SignalFormer模型在粗粒度识别任务中实现了令人印象深刻的97.57%和98.03%的识别准确率，在细粒度识别任务中实现了97.48%和98.16%的识别准确率。此外，我们引入了类增量学习评估，以证明SignalFormer在处理以前未见过的无人机信号类别的能力。上述结果共同表明，所提出的方法是一种以可靠方式支持ADI任务的有前途的解决方案。
