摘要
针对高斯混合模型估计非高斯系统时高斯混合项呈指数级增长问题,提出一种基于相似分布特性准则的聚类-合并方法。通过分析高斯混合项的分布特性,基于扩展积分均方误差代价函数搜索最优置信范围,并对混合项进行高斯聚类,进而获得具有不同分布特性的高斯簇。为防止高斯簇间对高斯子项的重复利用,引入局部最近邻思想对交叉高斯项进行重新分配。采用并行多元素合并方法对高斯簇中的混合项进行合并,在保证无偏性基础上减少下一时刻混合项数量。仿真结果表明,改进算法在保证跟踪精度的同时还可有效提高算法效率。
To solve the problem of the exponential growth of the Gaussian mixture components for estimating the state of the non-Gaussian system with the Gaussian mixture model,a cluster-fusion algorithm based on the similarity distribution criterion was proposed.According to that criterion,the Gaussian components were then clustered into different Gauss clusters based on the optimal confidence interval,derived by minimizing the extended integral square error cost function.Meanwhile,to avoid the reuse of the cross components,the local nearest neighbor approach was introduced to re-allocate these cross ones.Then,the components in the clusters were merged by the multi-element mergence method to keep with the unbiased property,which can decrease the number of the mixture components sharply.The results show that the proposed algorithm can not only reduce the running time,but also guarantee the tracking performance with a proper confidence interval.
作者
徐洋
方洋旺
伍友利
张丹旭
XU Yang;FANG Yangwang;WU Youli;ZHANG Danxu(Aeronautics Engineering College,Air Force Engineering University,Xi′an 710038,China)
出处
《国防科技大学学报》
EI
CAS
CSCD
北大核心
2019年第4期156-164,共9页
Journal of National University of Defense Technology
基金
航空科学基金资助项目(20175596020)
关键词
高斯混合模型
相似分布准则
高斯聚类
扩展积分均方误差代价函数
多元素融合
Gaussian mixture model
similarity distribution criterion
Gauss cluster
extended integral square error cost function
multi-element fusion
