基于DBN特征提取的雷达辐射源个体识别

针对传统特征参数难以表征复杂体制雷达信号个体特征的问题,基于深度置信网络DBN的深层特征提取和高维数据处理能力,提出一种基于DBN特征提取的雷达辐射源个体识别算法。首先建立基于多层受限玻耳兹曼机的DBN模型,然后通过DBN无监督提取脉冲包络前沿特征,再利用标签数据对模型参数进行有监督微调完成训练,最后输入未知辐射源信号脉冲包络前沿特征实现辐射源个体识别。与传统算法相比,该方法能够自适应地提取脉冲深层次细微差异,提取过程减少了对人为经验的依赖。实验结果表明,该算法对脉冲包络特征提取效果明显,有较高的识别精度。

Radar Specific Emitter Identification Based on DBN Feature Extraction

Aimed at the problem that the traditional characteristic parameters have difficulty in characterizing the specific characteristics of complex system radar signals, a specific emitter identification algorithm based on deep belief network feature extraction is proposed on account of the deep feature extracting and high dimensional data processing ability of deep belief network. Firstly, a DBN model based on multi layer restricted Boltzmann machine is established. Then, unsupervised extraction of pulse envelope frontier is realized via deep belief network. After that, the model parameters are fine tuned with label data in a supervised way to complete the training. Finally, the pulse envelope frontier features of the unknown source signals are input to realize the radar specific emitter identification. Compared with the traditional algorithm, the novel algorithm can adaptively extract from deep pulse features, and can also reduce the process of feature extraction to the dependence on human experiences. The experimental results show that the proposed algorithm provides satisfactory performance of pulse envelope feature extraction and higher recognition accuracy. The validity and application value of the algorithm are verified.