摘要
针对离散非线性系统的状态平滑问题,基于Rauch-Tung-Striebel(RTS)理论设计了一种容积卡尔曼平滑器(Cubature Kalman Smoother,CKS),即容积Rauch-Tung-Striebel平滑器(RTSCKS)。首先,基于经典贝叶斯状态估计理论框架,推导了状态概率密度分布形式的非线性系统最优平滑算法;其次,基于Rauch-Tung-Striebel理论,建立了相应的最优平滑递推算法;然后,将其与容积卡尔曼滤波算法相结合,建立了递推形式的RTS-CKS平滑器;最后,通过典型的纯方位跟踪模型验证了该平滑器的可行性和有效性。该平滑器为非线性系统的状态估计提供了新的估计算法。
In view of the state smoothing problem of nonlinear discrete-time system, a cubature Kalman smoother is derived based on the Rauch-Tung-Striebel theory,namely,the cubature Rauch-Tung-Striebel smoother( RTS-CKS) . Firstly,based on the classical Bayesian state estimation framework,the optimal smoot-hing algorithm of the nonlinear system is derived under the state probability density distribution form. Second-ly,the corresponding optimal smoothing recursion algorithm is established based on the Rauch-Tung-Striebel theory. Then,the recursion type form of RTS-CKS smoother is derived through the combinations of the cuba-ture Kalman filter and the optimal smoothing recursion algorithm above. Finally,the simulation shows the fea-sibility and effectiveness of the proposed smoother through classical bearings only tracking model. The pro-posed smoother provides a novel estimation algorithm for state estimation of nonlinear system.
出处
《电讯技术》
北大核心
2014年第11期1468-1474,共7页
Telecommunication Engineering
