自识别自校准Kalman滤波方法

Self-Recognition and Self-Calibration Kalman Filtering Method

在导航滤波、故障诊断等许多工程领域中,受环境因素影响、模型和参数的选取不当等原因,系统状态方程中往往含有未知输入(系统误差),传统的Kalman滤波方法无法消除这种未知输入的影响,导致产生较大的滤波误差。为此,提出一种自识别自校准Kalman滤波方法,并分别对线性系统和非线性系统进行了详细讨论,给出了相应的公式和滤波步骤。该方法能够自动识别状态方程中有无未知输入,当有未知输入时,则能自动估计未知输入,并对它进行补偿和修正。大量实例计算和仿真模拟表明,与传统方法相比,本文方法能够有效提高状态估计精度,且计算简单,便于工程应用。

Abstract:In many engineering fields,such as deep space exploration,navigation,fault diagnosis and so on,due to the influence of environmental factors,improper selection of models and parameters,the system state equation often contains unknown inputs(systematical errors).Traditional Kalman filters cannot eliminate the influence of unknown inputs,resulting in larger filtering errors.In this paper,a self-recognition and self-calibration Kalman filtering method is proposed.The linear and nonlinear systems are discussed,and the corresponding formulas and filtering steps are given.This method can automatically recognize whether there are unknown inputs in the state equation.When there are unknown inputs,they can beautomatically estimated,compensated and corrected them.A large number of examples and simulation results show that compared with the traditional method,the proposed method can effectively improve the accuracy of state estimations,and the calculation is simple,which is convenient for engineering application.