摘要
对于带有色观测噪声、公共干扰噪声和量测偏差的多传感器线性定常系统,应用协方差交叉融合估计算法,提出批处理协方差交叉融合(BCI)稳态Kalman滤波器和序贯协方差交叉融合(SCI)稳态Kalman滤波器,此方法可避免计算局部滤波误差互协方差,减小计算负担。特别,相比于每个局部滤波器的精度,所提出的协方差交叉融合Kalman滤波器的精度高。通过引入协方差椭圆给出了协方差交叉融合滤波器精度关系的几何解释。为了验证理论精度关系的正确性,最后给出一个跟踪系统Monte-Carlo仿真例子。
For multisensor linear time-invariant systems witli colored measurement noises,the common disturbance noises and measurement biases,applying the covariance intersection fusion estimation algorithm,the batch covariance intersection fusion(BCI)steady-state Kalman filters and the sequential covariance intersection fusion(SCI)steady-state Kalman filters are presented,which can avoid the computation of tiie local filtering errors and reduce the computational burden significantly.Specially,compared witii each local Kalman filters,the accuracy of the proposed covariance intersection fusion Kalman filters is higher.The geometric interpretation of the accuracy relations is given by the covariance ellipses.Finally,a Monte-Carlo simulation example for a tracking system verifies the correctness of the theoretical accuracy relations.
作者
齐文娟
QI Wen-Juan(School of Mechanical and Electrical Engineering,Heilongjiang University,Harbin,150080,China)
出处
《黑龙江大学工程学报》
2018年第1期53-59,共7页
Journal of Engineering of Heilongjiang University
基金
国家自然科学基金资助项目(61703147)
黑龙江省自然科学基金资助项目(F2016033)
黑龙江大学青年科学基金资助项目(QL2015)
黑龙江省高校基本科研业务费黑龙江大学专项资金项目
关键词
多传感器信息融合
批处理协方差交叉融合
序贯协方差交叉融合
精度比较
加权融合
协方差椭圆
multisensor information fusion
batch covariance intersection fusion
sequential covariance intersection fusion
accuracy comparison
weighted fusion
covariance ellij^se
作者简介
（1984-)，女，山东淄博人，讲师，博士研究生，研究方向：多传感器信息融合、鲁棒卡尔曼滤波方法，E-mail:2015072@hlju.edu.cn。
