基于模糊函数等高线与栈式降噪自编码器的雷达辐射源信号识别

Radar emitter signal recognition based on ambiguity function contour lines and stacked denoising auto-encoders

针对当前复杂体制雷达辐射源信号识别方法抗噪性能差、识别率低等问题,提出一种基于模糊函数等高线与栈式降噪自编码器的新识别方法。首先对辐射源信号的模糊函数进行高斯滤波并根据线性插值计算等高线,然后采用主成分分析方法降低其特征维度,保留主要模糊能量信息,最后构建深度学习栈式降噪自编码器,学习并提取等高线深层、泛在的特征,并通过Softmax分类器进行分类识别。实验结果表明,该方法在信噪比为0 dB时对6类典型雷达信号的整体平均识别率均保持在99.83%以上,即便是在-6 dB环境中,识别率也可达到83.67%,验证了所提方法在极低信噪比条件下良好的性能和可行性。

The complex radar emitter signal recognition methods have problems of poor anti-noise performance, low recognition rate, etc. To address these issues, we propose a new recognition method based on ambiguity function contour lines and stacked denoising auto-encoders. First, the ambiguity function is processed by the Gaussian smoothing and the contour lines are calculated by linear interpolation. Then, principal component analysis is used to reduce its feature dimension. The main ambiguity energy information is remained. Finally, deep learning stacked denoising auto-encoders are established to learn and extract the deep and more ubiquitous features of contour lines. The Softmax classifier is used to classify them. Simulation experiments show that the overall average recognition rates of six types of typical radar signals are all above 99.83% when the signal-noise ratio is 0 dB. The recognition rate can also reach 83.67% when the signal-noise ratio is-6 dB. Results prove that this method has good performance and feasibility under the extremely low signal-noise ratio conditions.