非线性状态方程自校准滤波方法

Nonlinear state equation self-calibration filtering method

针对工程实际中遇到的非线性系统状态方程中含未知输入(如环境因素的影响、模型和参数选取不当等)的情况,采用自校准技术,基于秩滤波与无迹Kalman滤波算法提出了一种非线性状态方程自校准滤波方法,并分别讨论了自校准秩滤波(SRF)与自校准无迹Kalman滤波(SUKF)两种情况。大量仿真结果和工程应用表明:与无迹Kalman滤波(UKF)相比,该方法通过对系统状态方程中的未知输入进行自动估计和补偿,改善了系统受未知输入影响下的滤波效果,从算例中可以看到,估计精度至少提高了80%,且计算简单,便于工程应用。

In view of the situation that system state equations are influenced by unknown inputs(such as environmental influence,improper selection of models or parameters,and etc.),a nonlinear self-calibration filtering recursive method for state equations with unknown inputs was proposed based on two nonlinear Kalman filtering methods,the self-calibration rank filter(SRF)and the self-calibration unscented Kalman filter(SUKF)were discussed respectively.According to numerous numerical simulation results and engineering applications,by estimating and compensating the unknown inputs in state equations automatically,the proposed algorithm can improve the filtering effect of the system under the influence of unknown inputs,and the estimation accuracy increased by 80% when compared with the unscented Kalman filtering method(UKF).Moreover,the calculation was simple and convenient for engineering applications.