To enhance the modulation recognition performance of emitter signals under low signal-to-noise ratio (SNR), a recognition system based on secondary time-frequency distribution, discriminative projection, and collaborative representation is proposed. Firstly, a novel time-frequency processing method, including sparse-domain noise reduction and secondary feature extraction, is proposed to reduce noise interference and information redundancy in time-frequency images. In this way, secondary time-frequency distribution with high stability and detailed representation is obtained. Then, the classifier based on discriminative projection and collaborative representation was designed to enhance the ability of low-dimensional representation and between-class discrimination, which optimised using the mini-batch random gradient descent method. As shown in the simulation, the overall average recognition success rate of this system aiming at eight types of emitter signals reaches 95.6% at the SNR of -8 dB. Results of simulation and analysis indicate the superiority of the proposed classification system in terms of robustness, timeliness, and adaptability.

中文翻译：

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基于判别投影和协同表示的发射器信号调制识别

为了提高低信噪比（SNR）下发射器信号的调制识别性能，提出了一种基于二次时频分布，判别投影和协同表示的识别系统。首先，提出了一种新的时频处理方法，包括稀疏域降噪和二次特征提取，以减少时频图像中的噪声干扰和信息冗余。以此方式，获得了具有高稳定性和详细表示的二次时频分布。然后，设计了基于判别投影和协同表示的分类器，以增强低维表示和类间区分的能力，并使用小批量随机梯度下降法对其进行了优化。如仿真所示，该系统针对八种发射器信号的总体平均识别成功率在SNR为-8 dB时达到95.6％。仿真和分析结果表明，提出的分类系统在鲁棒性，及时性和适应性方面具有优势。