摘要
针对多传感器数据的多样性,提出一种改进的数据融合算法.首先,利用小波技术消除已收集数据的高斯白噪声并对数据进行压缩;其次,对处理后的数据进行分层,并对系数进行Kalman滤波,同时利用Mallat快速重建算法重构数据;最后,利用最大、最小贴近度计算传感器数据的信噪比,并通过信噪比进行数据融合.基于实际采集的多传感器数据对比实验结果表明,该数据融合算法在稳定性上优于简单加权数据融合、小波数据融合和Kalman滤波融合等算法.
Aiming at the diversity of multi-sensor data,we proposed an improved data fusion algorithm.Firstly,the wavelet technology was used to eliminate the Gaussian white noise of collected data and compress the data.Secondly,the processed data was stratified,and the coefficients were filtered by Kalman.Meanwhile,the data was reconstructedby the Mallat fast reconstruction algorithm.Finally,the signal-to-noise ratio(SNR)was calculated bymaximum and minimum degree of close to the sensor data,and data fusion was carried out by SNR.Experimental results based on multi-sensor actual data show that the data fusion algorithm is superior to simple weighted data fusion,wavelet data fusion and Kalman filtering fusion algorithm in stability.
作者
陈英
胡艳霞
刘元宁
朱晓冬
CHEN Ying;HU Yanxia;LIU Yuanning;ZHU Xiaodong(College of Software,Nanchang Hangkong University,Nanchang 330063,China;College of Computer Science and Technology,Jilin University,Changchun 130012,China)
出处
《吉林大学学报（理学版）》
CAS
CSCD
北大核心
2018年第5期1170-1178,共9页
Journal of Jilin University:Science Edition
基金
国家自然科学基金(批准号:61762067)
江西省自然科学基金(批准号:20161BAB212034)
江西省教育厅项目(批准号:GJJ160692)
关键词
多传感器数据
数据融合
信噪比
multi-sensor data
data fusion
signal-to-noise ratio
