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提出一种自校准扩展Kalman滤波(SEKF)方法,针对3种含有未知输入(如未知系统误差、突风、故障等)的不同的非线性系统模型,分别给出了滤波递推算法.在导航、信号处理、故障诊断等领域的许多非线性工程中,传统的扩展Kalman滤波(EKF)方法无法消除未知输入的影响,在滤波过程中往往产生较大误差甚至发散.提出的SEKF方法能够对这种未知输入进行补偿和修正,从而提高滤波精度.数值仿真算例表明:SEKF的滤波误差均值和标准差分别减少到传统EKF的1/12和1/4,有效地改善了滤波精度.并且该方法计算简单,便于工程应用.
A self-calibrating extended Kalman filter (SEKF) method is proposed, and three different nonlinear system models with unknown inputs (such as unknown system error, gust, fault, etc.) are given respectively. , Signal processing and fault diagnosis, the traditional Extended Kalman Filter (EKF) method can not eliminate the influence of unknown input and often produces large errors and even divergence in the filtering process.The proposed SEKF method can correct this The unknown input is compensated and corrected to improve the filtering accuracy.The numerical simulation shows that the mean and standard deviation of SEKF are reduced to 1/12 and 1/4 of the traditional EKF respectively and the filtering accuracy is effectively improved The method is simple and easy to use in engineering.