一类非线性广义随机系统的降阶Kalman滤波器

Reduced-order Kalman Filters for a Class of Nonlinear Singular Stochastic Systems

研究了一类非线性广义系统的状态估计问题。利用Taylor级数展开的方法将其转化为线性广义系统;再利用奇异值分解,对线性化后的系统进行降阶,转化为等价的正常线性系统;最后基于Kalman滤波估值理论,得到非线性广义系统的Kalman滤波器。通过数值仿真例子,验证了所提方法的有效性。

The state estimation problem is considered for a class of nonlinear singular systems.The linearized model was obtained by using Taylor＇s series expansion method;based on singular value decomposition,it was changed into a order-reduced equivalent normal system.By classical Kalman filtering theory,the state Kalman estimators for the nonlinear singular system was obtained.A numerical example shows the effectiveness of the proposed algorithm.