Constraining Kalman Filter and Smoothing Estimates to Satisfy Time-Varying Restrictions
提出一种通过增广测量方程的方法，将已知约束（时不变、时变、线性或非线性）纳入卡尔曼滤波过程，从而提高状态向量估计效率。
It sometimes happens that the unobservable state vector of a linear dynamic model expressed in the state space is subject to known restrictions. Incorporation of this information into the Kalman filter procedure will increase the efficiency of estimation. It is shown that a simple augmentation of the measurement equation constrains the estimated state vector to obey the restrictions. The method applies whether the restrictions are time-invariant, time-varying, linear, or nonlinear. Copyright 1992 by MIT Press.