Multiple Model Adaptive Estimator for Nonlinear System with Unknown Disturbance

A multiple model adaptive estimator (MMAE) is presented for nonlinear systems with unknown disturbances. Multiple models are constructed with a set of process noise covariance matrices, such that the algorithm can adapt to different levels of unknown disturbances. The performance of the MMAE is analyzed for the considered system. It is proved that, under certain assumptions, the MMAE keeps the dynamics of its estimation error stable. A performance comparison among different estimators is carried out for space surveillance, where the position of a space target is estimated by using double line‐of‐sight measurements. Simulation studies illustrate that the presented algorithm outperforms the extended Kalman filter and the nonlinear robust filter.