摘要
为提高多传感器数据融合的精度和速度,结合凸组合融合算法与协方差交叉融合算法,并采用果蝇优化算法优化协方差交叉融合算法的融合系数,提出一种改进协方差交叉融合算法,实现了多传感器数据的快速、精确融合。仿真结果表明,所提算法在x轴和y轴上的数据融合均方根误差约为3 m,融合的时间约为0.44 s,相较于多贝叶斯估计、模糊聚类、极大似然估计等数据融合算法,具有明显优势,提高了多传感器数据融合的精度和速度。
In order to improve the accuracy and speed of multi-sensor data fusion,a convex combination fusion algorithm and covariance cross fusion algorithm were combined,and the fusion coefficient of the covariance cross fusion algorithm was optimized using the fruit fly optimization algorithm.An improved covariance cross fusion algorithm was proposed,which achieved fast and accurate fusion of multi-sensor data.The simulation results show that the root mean square error of the proposed algorithm for data fusion on the x-axis and y-axis is about 3 m,and the fusion time is about 0.44 s.Compared with data fusion algorithms such as multi Bayesian estimation,fuzzy clustering,and maximum likelihood estimation,it has significant advantages and improves the accuracy and speed of multi-sensor data fusion.
作者
王张夫
汤显峰
Wang Zhangfu;Tang Xianfeng(Quzhou College of Technology,Quzhou 324000,China;Center of Information Technology,Zhejiang University,Hangzhou 310027,China)
出处
《电子测量技术》
北大核心
2024年第8期78-85,共8页
Electronic Measurement Technology
基金
国家自然科学基金青年基金(61602141)
国家自然科学基金青年基金(61503336)项目资助。
关键词
分布式系统
多传感器数据融合
协方差交叉融合
凸组合融合算法
果蝇优化算法
distributed systems
multi sensor data fusion
covariance cross fusion
convex combination fusion algorithm
drosophila optimization algorithm
