一种改进型残差约束渐消采样滤波器

An Improved Fading Sigma-Point Filter with Residual Constraints for Nonlinear Systems

非线性系统的状态方程和观测方程不准确时,非线性自适应采样滤波器的滤波精度将偏离真实值;严重时将引起滤波器的发散,得到完全虚假的滤波值,失去了滤波原本的意义。为减小线性化误差对非线性系统状态估计的影响,本文采用采样滤波器中的UKF(Unscented Kalman Filter)方法对非线性系统进行高精度滤波。同时深入研究UKF的自适应渐消记忆策略,以增强对建模误差的鲁棒性和对突变状态的跟踪能力,提高状态估计的精度和快速性。本文针对上述滤波方法进行改进,并通过一个非线性系统滤波仿真试验,证明其有效性。

If the state equation and the observation equation are not elaborately designed, the filtering accuracy of nonlinear adaptive sigma-point filter will deviate from actual value propagated through designated model. In the case of deteriorated modeling for the strong nonlinear system, it leads to distorted estimation thereby failing to meet the anticipated aim of the state estimator. On one hand, in order to diminish the repercussion of linearization error on estimation of state of nonlinear system, this paper adopts Unscented Kalman Filter, a division of Sigma-Point Filter, to implement filtering of nonlinear system with high level of accuracy. On the other hand, the aim of researches on the adaptive fading and strong tracking strategy is to enhance the robustness to neutralize errors brought by the inaccurate system models and the capability of tracking the abrupt state. A newly improved filter is developed and demonstrated in this paper, which is proved by a computer simulation of filtering the state of a nonlinear system.