基于多模态深度学习的信号调制识别

Signal modulation recognition based on multimodal depth learning

摘要:

信号调制识别技术在民用和军事领域都有重要应用。当前信息化战场中，由于各类雷达、通信、导航、电子战武器等信息辐射源的数量愈来愈多，调制形式也日益多样化，信号密度愈来愈大，战争电磁环境日趋复杂化，传统的信号调制识别技术已无法适应。因此，提出基于深度学习的AlexNet网络和复数神经网络，同时采用多模态特征融合和模型融合技术，融合信号统计图域和信号I/Q波形域的多模态信息，实现信号调制识别。仿真结果表明，所提方法的识别精确度在不同信噪比下均优于单模态识别方法和未采用多模态协同融合框架的方法。

Abstract:

Signal modulation identification technology has important applications in both civilian and military fields. In the current information battlefield, due to the increasing number of information radiation sources such as various radars, communications, navigation, and electronic warfare weapons, the modulation forms are becoming more and more diverse, and the signal density is increasing, which makes the electromagnetic environment of war increasingly complicated, therefore the traditional signal modulation identification technology has been unable to adapt. A robust feature extraction, fusion and recognition technology of complex communication modulation signals is put forward, and a deep learning-based AlexNet network and complex neural network are proposed. Multimodal information in the statistical graph domain and signal I/Q waveform domain is fused for signal modulation identification. The simulation results show that the recognition accuracy of the proposed method is higher than that of the single-modal recognition method and the method without the multi-modal collaborative fusion framework under different Signal-to-Noise Ratios(SNRs).