摘要
针对杂波、漏检等环境下的多机动目标估计问题,提出了两种非线性系统模型下多模型多伯努利(MM-MB)滤波器的实现方法.首先,基于容积卡尔曼滤波算法给出了MM-MB滤波器的容积卡尔曼高斯混合(CK-GM)实现过程;然后,为了增加CK-GM-MM-MB滤波算法的数值稳定性,给出了MM-MB滤波器的平方根容积卡尔曼高斯混合实现过程;最后,通过仿真实验验证了所提方法的有效性.
Two implementations of the multiple‐model multi‐Bernoulli （MM‐MB） filter were proposed for nonlinear system models in the presence of clutter and missed detection .Based on the cubature Kalman filtering algorithm ,the cubature Kalman Gaussian mixture （CK‐GM ） implementation of the MM‐MB filter was proposed .Moreover ,to improve the numerical stability of the CK‐GM‐MM‐MB filtering algorithm ,the square‐root CK‐GM implementation of the MM‐MB filter was proposed .Fi‐nally ,a numerical example was presented to verify the effectiveness of the proposed approaches .
出处
《华中科技大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2015年第9期7-12,18,共7页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
航空科学基金资助项目(20132076002)
关键词
非线性模型
机动目标
多模型多伯努利滤波器
容积卡尔曼
高斯混合
nonlinear models
maneuvering targets
multiple-model multi-Bernoulli filter
cubature Kalman
Gaussian mixture
