摘要
传统单一线性或非线性滤波方法往往难以获得最优线性/非线性混合动态系统状态估计,针对这一问题,结合卡尔曼滤波(KF)方法可获得线性状态估计最优解、计算量小等优势,提出了一种基于KF和扩展容积卡尔曼滤波(A-CKF)的组合滤波方法。该方法将系统状态分解为线性状态与非线性状态两部分,分别采用KF和简化两次扩展容积卡尔曼滤波(STA-CKF)方法进行系统状态估计。机动目标跟踪和捷联惯性导航系统非线性对准仿真结果表明,相比于Rao-Blackwellized粒子滤波方法,新方法在保证滤波精度的前提下,使得计算成本有效降低;相比于STA-CKF方法,新方法在滤波精度和滤波实时性方面均得到明显提高。
To solve the problem that the pure linear or nonlinear filter is difficult to obtain the optimal state estimation results of a hybrid linear/nonlinear dynamic system, a novel hybrid dynamic filter based on Kalman filter （KF） and Augmented Cubature KF （A-CKF） was proposed by utilizing the advantages that KF can obtain optimal solution for linear state and its computation cost is low. In the presented filter, the system state was decomposed into linear and nonlinear parts which were estimated by the KF and the Simplified Twice Augmented Cubature KF（STA -CKF） respectively. The simulation results of the maneuvering target tracking and the strapdown inertial navigation system nonlinear initial alignment cases show that the novel filter had similar filtering accuracy to the Rao-Blackwellized particle filter but lower computation cost. Compared with the pure STA-CKF, its accuracy and real-time performance were improved significantly.
出处
《红外与激光工程》
EI
CSCD
北大核心
2014年第2期647-653,共7页
Infrared and Laser Engineering
基金
国家863计划(2010AA7010213)
关键词
组合滤波
容积卡尔曼滤波
卡尔曼滤波
目标跟踪
捷联惯性导航系统
初始对准
integrated filter
cubature Kalman filter
Kalman filter
target tracking
strapdown inertial navigation system
initial alignment
