Joint estimation of state and bias based on generalized systematic model
Jie Zhou, Yan Liang, Lin Zhou, Quan Pan · 2015
This paper presents a joint estimation of state and bias based on generalized systematic model. Registration process is implemented as follows: first of all, augment method is utilized to derive dynamic equation of the system. Then, structure unknown inputs induced by the dynamic equation of the bias are decoupled. Unbiased estimation of the state and bias is finally obtained by the augment minimum mean squared estimation (AMMSE). The simulation proves that the proposed method is not only effective but also efficient by comparing with other methods, respectively.