西安电子科技大学学报（自然科学版）2011，Vol.38Issue(1)：104-109,6.DOI:10.3969/j.issn.1001-2400.2011.01.017
一种迭代收缩非线性状态约束滤波算法
Iterative shrinking filtering algorithm with nonlinear state constraints
摘要
Abstract
In the process of filtering, the filtering accuracy can be improved if the state constraints are used in an effective manner. The nonlinear constrained function can be linearized by the Taylor series expansion.However, if the Jacobian matrix of the nonlinear constrained function is nonexistent, this method will not work anymore. Moreover, the moving horizon estimation (MHE) algorithm needs a heavy computational burden in this condition. So a perfect measurement method is proposed based on the unscented transform to solve this problem. Furthermore, in order to reduce the negative effect from the base point error, the nonlinear constraints can be treated as measurements with different noise covarianee. The noise eovariance shrinks in the measurement update stage, and the constrained conditions are enhanced step by step. The state estimation error is improved after some iterations. Simulation results show that the proposed algorithm can obtain a higher filtering accuracy, and that its computational time is 1/27 of that of the moving horizon estimation algorithm even if the window size is 2.关键词
非线性状态约束/U变换/状态估计/滤波/信息融合分类
信息技术与安全科学引用本文复制引用
陈金广,李洁,高新波..一种迭代收缩非线性状态约束滤波算法[J].西安电子科技大学学报（自然科学版）,2011,38(1):104-109,6.基金项目
国家自然科学基金资助项目(60832005,60702061) （60832005,60702061）
陕西省教育厅自然科学专项资助项目(2010JK565) （2010JK565）
