基于栈式稀疏降噪自编码网络的辐射源调制识别

针对辐射源识别中噪声敏感和识别能力不足等问题，提出了一种基于核空间时频特征与栈式稀疏降噪自编码网络的识别系统.通过时频变换、稀疏域降噪和核空间降维投影降低噪声干扰和特征冗余，基于降噪自编码与稀疏自编码思想构建栈式稀疏降噪自编码识别网络.实验结果表明系统在识别率和时效性上综合性能最优，能够显著降低噪声敏感性，低信噪比环境下适应性较强.当信噪比为-12dB时，系统对8类辐射源信号的整体平均识别率达到96.75%.

Emitter Signal Modulation Recognition Based on Stacked Sparse Denoising Auto-Encoders

To enhance the classification performance and noise sensitivity of emitter signal recognition,a recognition system based on kernel space time-frequency feature and stacked sparse denoising auto-encoders (SSDAE) is proposed.Firstly,the noise interference and feature redundancy reduced by time-frequency transform,sparse-domain denoising and kernel space dimensionality reduction.Then,it is based on the idea of sparse auto-encoder (SAE) and denoising auto-encoder (DAE),an SSDAE based recognition network is constructed.Experimental results show that the system has the best comprehensive performance in recognition rate and time efficiency,which can significantly reduce noise sensitivity and improve low SNR environment adaptability.When the SNR is -12dB,the overall average recognition rate of the system for the 8 types of emitter signals reaches 96.75%.