Spacecraft relative navigation based on multiple model adaptive estimator

This paper studies the multiple model adaptive estimator (MMAE) for nonlinear systems with unknown disturbances. Multiple models are constructed with a set of process noise covariance matrices, such that the algorithm that consists of multiple parallel filters can adapt to different levels of unknown disturbances. The filtering stability of the MMAE is analyzed. Sufficient conditions to ensure the boundedness of the algorithm is provided. A performance comparison among an extended Kalman filter (EKF), a nonlinear robust filter (NRF) and the MMAE is carried out for spacecraft relative navigation, where the position of a space target is estimated by using double line-of-sight (LOS) measurements. Simulation studies illustrate that the MMAE performs better than the EKF and the NRF.