基于模糊函数主脊切片和深度置信网络的雷达辐射源信号识别

雷达辐射源信号识别是电子侦察系统的关键组成部分,为了提高低信噪比条件下对低截获概率雷达信号识别的准确率,提出了一种基于模糊函数主脊切片(MRSAF)与深度置信网络(DBN)的雷达辐射源信号识别方法。首先对雷达信号进行奇异值分解(SVD)进行降噪预处理,求解雷达信号的模糊函数并提取其主脊切片包络,采用奇异值分解方法降低噪声对主脊切片包络的影响,然后建立基于受限波尔兹曼机的DBN模型并运用标签数据有监督微调模型参数完成训练,最后基于该算法模型实现辐射源信号的分类和识别。仿真结果表明:该方法在低信噪比条件下也有较高的识别率,信噪比高于-4dB时,识别率可以达到90%以上,验证了本算法的有效性和应用价值。

A Recognition Method of Radar Emitter Signals Based on SVD of MRSAF and DBN

Recognition of radar emitter signals is one of core elements in radar reconnaissance systems. In order to attain a higher correct recognition rate of radar emitter signals under condition of low signal to noise (SNR) ratio, a novel method based on Main Ridge Slice of Ambiguity Function (MRSAF) and Deep Belief Network (DBN) is presented. Firstly, the singular value decomposition (SVD) is preprocessed for noise reduction, and then this paper calculates the AF of the sorted signal and ascertains the main ridge slice envelope. To improve the recognition performance, the SVD is employed to eliminate the influence of noise on the main ridge slice envelope. A DBN model is established on the stacked Restricted Boltzmann Machines (RBM) and the labeled data with the supervisory fine tuning model parameters are used to complete the training. Finally, the model is used to achieve the radar emitter signals recognition and classification. The simulation results indicate that the novel algorithm provides significant performance and the validity and application value of this algorithm are verified. Compared to the existing methods, the novel method can achieve a higher correct recognition rate even at a low SNR.