Constrained Kalman Filtering for Nonlinear Dynamical Systems with Observation Losses
In this paper, the constrained extended Kalman filter (EKF) is discussed for nonlinear dynamical systems when observations are available according to a Bernoulli process. First, by using EKF approach, we provide a sufficient condition such that computing of the error covariance of nonlinear system is converted into the corresponding computing of linear system. Then based on physical consideration, at each time step through projecting the unconstrained Kalman filter solution onto the state constraint surface, the constrained estimation can be derived, which significantly improves the prediction accuracy of the filter. We study the statistical convergence properties of the error covariance matrix, showing the existence of a critical value for the arrival rate of the observation, beyond which a transition to an unbounded state error covariance occurs. We show that, when the system observation matrix restricted to the observable subspace is invertible, the critical probability is an exact value. Simulations are provided to demonstrate the effectiveness of the theoretical results.
Extended Kalman Filter State Estimation Inequality Constraints Missing Observation
Zhen Luo Huajing Fang Lisha Xia
Department of Control Science and Engineering, Huazhong University of Science and Technology,Wuhan 4 Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan
国际会议
The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)
太原
英文
2984-2989
2012-05-23（万方平台首次上网日期，不代表论文的发表时间）