基于经验模态分解的大数据波束形成方法

Beamforming Method Based on Empirical Mode Decomposition

通过对大数据进行波束形成处理,提高大数据的挖掘和聚类能力,传统方法采用模糊C均值聚类进行大数据波束形成,抗干扰性能不强,提出一种基于经验模态分解的大数据波束形成方法。首先分析的大数据的数据结构和时间序列处理模型,然后通过经验模态分解实现对大数据序列的特征提取和波束形成。仿真结果表明,采用该方法进行大数据波束形成处理,提高了大数据挖掘和聚类性能,收敛性和抗干扰性较好。

Through the large data processing, improve the data mining and clustering ability. In the traditional method, the large data beamforming method based on empirical mode decomposition is proposed by using the fuzzy C means clustering to improve the data mining and clustering ability. The data structure and time series processing model of large data is analyzed, and then the feature extraction and beamforming of large data sequence are realized by empirical mode decomposition. The simulation results show that the method is used to deal with large data beamforming, which improves the performance of large data mining, clustering performance, convergence and better anti-interference.