摘要
为有效解决捷联惯性导航系统对准过程中异常量测对状态估计精度的影响,该文提出一种基于稳健估计的稀疏网格积分滤波算法(RESGQF)。该文给出了精度为3级的稀疏网格采样点规则,引入稳健估计算法,构建针对对准系统各状态分量偏差的权重函数。基于稀疏网格积分滤波(SGQF)算法框架,利用权重矩阵实时对量测噪声进行更新,从而降低异常量测对系统状态的影响。通过模拟飞行器动机座空中对准过程,对比在复杂噪声环境下不同滤波方法的性能,证明所提算法提升了系统鲁棒性。
To effectively address the impact of abnormal measurements on the accuracy of state estimation during the alignment process of the strapdown inertial navigation system(SINS),this paper proposes an improved sparse-grid quadrature filter algorithm based on robust estimation(RESGQF).In this paper,a sparse-grid sampling point rule with accuracy of level-3 is given,and the robust estimation algorithm is introduced to construct a weight function for the deviation of each state component of the SINS alignment system.Based on the sparse-grid quadrature filter(SGQF)algorithm framework,the measurement noise is updated in real time by using the weight matrix,thereby reducing the impact of measurement outliers on system statement.By simulating the air alignment of the aircraft moving base,the performance of different filter methods under complex noise environments is compared,and it is verified that the proposed algorithm improves the robustness of the system.
作者
钱晨
高阳
陈庆伟
郭健
Qian Chen;Gao Yang;Chen Qingwei;Guo Jian(School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China)
出处
《南京理工大学学报》
CAS
CSCD
北大核心
2024年第5期568-577,共10页
Journal of Nanjing University of Science and Technology
基金
中国博士后科学基金(2023M741717)
江苏省卓越博士后计划(2023ZB660)
国防基础科研计划(JCKY2021606B002)
国家自然科学基金(62203222)
中央高校基本科研业务费专项资金(30922010407)。
关键词
稳健估计
稀疏网格积分滤波器
离群值
初始对准
鲁棒性
robust estimation
sparse-grid quadrature filter
outlier
initial alignment
robustness
