期刊
AUTOMATICA
卷 45, 期 1, 页码 25-33出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.automatica.2008.05.029
关键词
Kalman filter; Inequality constrained systems; State estimation; Function estimation; Interior point applications
Kalman-Bucy smoothers are often used to estimate the state variables as a function of time in a system with stochastic dynamics and measurement noise. This is accomplished using an algorithm for which the number of numerical operations grows linearly with the number of time points. All of the randomness in the model is assumed to be Gaussian, including other available information, for example a bound on one of the state variables, is non trivial because it does not fit into the standard Kalman-Bucy smoother algorithm. In this paper we present an interior point method that maximizes the likelihood with respect to the sequence of state vectors satisfying inequality constraints. The method obtains the same decomposition that is normally obtained for the unconstrained Kalman-Bucy smoother, hence the resulting number of operations grows linearly with the number of time points. We present two algorithms, the first is for the affine case and the second is for the nonlinear case. Neither algorithm requires the optimization to start at a feasible sequence of state vector values. Both the unconstrained affine and unconstrained nonlinear Kalman-Bucy smoother are special cases of the class of problems that can be handled by these algorithms. (C) 2008 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据