基于递归熵特征提取的层次化物联网数据检测

Hierarchical Internet of Things data detection based on recursive entropy feature extraction

为了提高层次化物联网数据的检测能力,提出了基于递归熵特征提取的检测方法。采用网格分块区域融合方法进行层次化物联网数据的存储结构分析,建立融合聚类模型,采用模糊相关性融合聚类方法进行数据调度,提取数据的递归熵特征量,采用层次化演化聚类方法进行数据的自适应分块匹配,并用匹配滤波检测方法进行数据检测过程中的干扰抑制,根据递归熵的规则性分布关系实现数据检测优化。仿真结果表明:采用该方法进行层次化物联网数据检测的抗干扰性较好,特征匹配能力较强,数据检测的准确率较高。

In order to improve the accurate detection capability of the hierarchical network data,a hierarchical object networking data detection method is give based on a recursive entropy feature extraction.The hierarchical evolution clustering method is adopted to carry out the self-adaptive blocking matching of the hierarchical object networking data by adopting a fuzzy correlation fusion clustering method.By adopting the matching filter detection method,the interference suppression in the detection process of the hierarchical Internet of Things data is carried out,and the hierarchical object networking data detection optimization is realized according to the regularity distribution relation of the recursive entropy.The simulation results show that the method has good anti-interference performance,high characteristic matching ability and high probability of data detection.