云计算下大规模非显著特征多传感数据融合系统设计

云计算环境下存在大规模非显著特征多传感数据,传统数据融合系统容易受到传感器自身干扰,融合结果不可靠。为此,设计了一种新的云计算下大规模非显著特征多传感数据融合系统。给出融合系统总体结构,介绍了非显著特征数据采集模块。利用模糊理论与协方差匹配技术对经典卡尔曼滤波技术中的噪声协方差进行修正,以滤除传感器自身噪声。在云计算环境中,以联合滤波结构为基础,实现云计算下大规模非显著特征多传感数据的融合。实验结果表明,所设计系统融合结果可靠、实用性强。

Design of large scale non salient features multi sensor data fusion system under cloud computing

in the cloud computing environment, there are large-scale non salient features, multi sensor data, traditional data fusion system is vulnerable to sensor interference, and the fusion results are unreliable. Therefore, a new large-scale non salient feature multisensor data fusion system under cloud computing is designed. The overall structure of the fusion system is given, and the non salient feature data acquisition module is introduced. Using the fuzzy theory and covariance matching technique, the noise covariance in the classical Calman filtering is modified to filter out the noise of the sensor. In the cloud computing environment, based on the joint filtering structure, the fusion of large-scale non salient features and multi-sensor data under cloud computing is implemented. The experimental results show that the fusion results of the system are reliable and practical.