High-accuracy Parallel Two-stage Estimator for Generalized Bias of Micro Sensor with Unknown Input

摘要

A high-accuracy parallel two-stage linear minimum-mean-square-error estimator (PTSMMSE) with free unknown input (UI) is first proposed to achieve joint identification of state and generalized bias for micro-electro-mechanical-system (MEMS) sensor. First, a UI-free bias dynamic model is derived. Then, PTSMMSE is constructed with two multi-dimensional filters. Simulation results demonstrate that the estimation error and RMSE (Root Mean Square Error) of the system bias and state are improved to about 3 and 5 times by the proposed method compared with that of Kalman filter (KF), respectively. The improved accuracy proves the effectiveness of the proposed method.