快速对角阵权系数协方差交叉融合容积卡尔曼滤波器  

Fast covariance intersection fusion weighted by diagonal matrix cubature Kalman filter

作　　者：刘金钢 郝钢[1] LIU Jin-gang;HAO Gang(Electronic Engineering Institute,Heilongjiang University,Harbin Heilongjiang 150080,China)

机构地区：[1]黑龙江大学电子工程学院,黑龙江哈尔滨150080

出　　处：《控制理论与应用》2023年第2期313-321,共9页Control Theory & Applications

基　　金：国家自然科学基金项目(61503127);黑龙江省高校基础研究基金项目(KJCX201901,KJCX201903,2021–KYYWF–0027);黑龙江大学杰出青年基金项目;黑龙江省信息融合估计与检测重点实验室项目资助。

摘　　要：针对互协方差信息未知的多传感器系统,本文提出了一种快速对角阵权系数协方差交叉融合算法(FDCI).本文首先提出了一种对角阵权系数协方差交叉融合(DCI)方案,并证明了所提出DCI算法在融合估计精度上高于经典批处理CI融合(BCI)算法.在此基础之上,针对非线性等复杂的互协方差未知的多传感器系统,提出FDCI算法,并证明了所提出FDCI算法的无偏性及鲁棒精度. FDCI融合算法虽然在融合估计精度上低于DCI,但FDCI无需进行多权系数的非线性代价函数的优化问题,进而大大降低了计算负担,提高了系统的实时性.最后,结合容积卡尔曼滤波算法(CKF)提出了快速对角阵权系数协方差交叉融合容积卡尔曼滤波算法.仿真实例验证了所提出算法的正确性和有效性.For multi-sensor systems with unknown cross-covariance,a fast covariance intersection fusion algorithm weighted by diagonal matrix(FDCI)is proposed.First,the covariance intersection fusion algorithm weighted by diagonal matrix(DCI)is proposed in this paper,and it is proved that the fusion estimation accuracy of the DCI algorithm is higher than that of the classical batch CI fusion(BCI)algorithm.Furthermore,for complex multi-sensor systems with unknown cross-covariances such as nonlinear systems,the FDCI algorithm is proposed,and its unbiasedness and robust accuracy are proved.Although the FDCI fusion algorithm has lower fusion estimation accuracy than DCI,the FDCI algorithm does not involve the optimization of nonlinear cost function with multiple weight coefficients,which greatly reduces the computational burden and improves the real-time performance of the system.Finally,combining with the cubature Kalman filter algorithm(CKF),a fast covariance intersection fusion weighted by the diagonal matrix CKF algorithm is proposed.A simulation example verifies the correctness and effectiveness of the proposed algorithm.

关 键 词：非线性系统 协方差交叉融合 容积卡尔曼滤波器 

分 类 号：TN713[电子电信—电路与系统]