自校准扩展Kalman滤波方法

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.

Self-calibration extended Kalman filter method

A self-calibration extended Kalman filter(SEKF)method was presented. Recursive algorithms of the SEKF were established for three nonlinear dynamic models with unknown inputs, such as unknown systematic error, gust and fault. In many nonlinear engineering cases, such as navigation, signal process, fault diagnosis, the conventional extended Kalman filter (EKF) cannot eliminate the effect of the unknown inputs, and maybe always lead to greater filtering errors or even diverge. The proposed SEKF is applied to compensate and correct the unknown inputs, and improve filtering accuracy. Numerical simulation shows that mean and standard deviation of state estimate errors of SEKF decrease to 1/12 and 1/4 respected to the conventional EKF, respectively, and the filtering accuracy is effectively improved. The SEKF method is simple to calculate and easy to apply in engineering.