一种迭代收缩非线性状态约束滤波算法

doi:10.3969/j.issn.1001-2400.2011.01.017

Abstract

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 covariance. The noise covariance 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.

Key words: nonlinear state constraints, unscented transform, state estimation, filtering, information fusion

CHEN Jinguang;LI Jie;GAO Xinbo. Iterative shrinking filtering algorithm with nonlinear state constraints[J].J4, 2011, 38(1): 104-109.

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Iterative shrinking filtering algorithm with nonlinear state constraints

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