基于核密度估计密度峰聚类的通信辐射源个体识别

Individual Communication Transmitter Identification Based on Density Peaks Clustering Algorithm via KDE

通信辐射源个体识别是通过发射机反映在信号上的差异来判别信号与辐射源个体之间的关联。传统的通信辐射源个体识别方法以及新兴的利用神经网络进行辐射源个体识别的方法都依赖带类别信息的信号样本,然而在实际中带类别信息的信号样本获取难度很大。为了解决这个问题,引入了无监督学习中的密度峰值聚类算法,在无类别信息信号样本的前提下进行通信辐射源个体识别。由于密度峰值聚类算法的性能受人工输入参数d c的影响较大,文中利用核密度估计(KDE)及热扩散方程改进算法,在不需要人工输入参数的条件下实现对数据的分类。文中所提算法在实际电台信号数据集上进行了实验,具有较好的效果,验证了该算法的可靠性和有效性。

Specific emitter identification(SEI)technique means that the relationship between the signal and the individual of the radiation source is judged by the difference in the signal of the transmitter response.Both the traditional methods and new emerging methods to make the neural networks for SEI rely on signal samples with category information.However,in conventional practice,the signal samples with category information are difficult to acquire.In order to solve this problem,this paper introduces a density peak clustering(DPC)algorithm in unsupervised learning to achieve SEI without classless information signal samples.Since the performance of the DPC algorithm is greatly influenced by the artificial input parameter d c,this paper utilizes the diffusion equation and the kernel density estimation improved algorithm for classifing the data without the need of manual input parameters.The algorithm proposed in this paper is good in effects,and reliable and effectiveness in algorithm.