Cubature卡尔曼滤波-卡尔曼滤波算法

Cubature Kalman filter-Kalman filter algorithm

针对条件线性高斯状态空间模型,提出cubature卡尔曼滤波-卡尔曼滤波算法(CKF-KF),分别应用CKF和KF估计模型中的非线性和线性状态.该算法对非线性与线性状态均进行cubature采样,并将两种样本通过线性方程和量测方程进行传播,以获得非线性状态估计.机动目标跟踪仿真结果表明,CKF-KF的估计精度比Rao-Blackwellized粒子滤波器(RBPF)略低,但算法运行时间不到其1%;与无迹卡尔曼滤波器(UKF-KF)相比,估计精度相当,但算法运行时间降低了22%,有效地提高了实时性.

A filtering algorithm, cubature Kalman filter-Kalman（CKF-KF） filter, is proposed for conditionally linear Ganssian state model, which respectively employs CKF and KF to estimate nonlinear state and linear state in the model. The above states are carried out cubature sampling, which are propagated through linear and observation equations to estimate nonlinear state. The maneuvering target tracking simulation results show that, compared to the Rao-Blackwellized particle filter（RBPF）, the algorithm running time of CKF-KF is less than 1% of that with a slightly lower filtering performance loss, and the estimation accuracy of CKF-KF coincides with that of UKF-KF, whereas the algorithm running time reduces by 22% and effectively improves real-time.