Multiple-model adaptive estimation for space surveillance with measurement uncertainty

An efficient multiple‐model adaptive estimation (MMAE) algorithm is presented for time‐variant system with both system and measurement uncertainties, whose statistics are supposed to be unknown. The model uncertainties are described by a set of noise covariance matrices, such that a small model set is sufficient to achieve good estimation performance. To demonstrate the feasibility of the presented MMAE for the considered time‐variant uncertain system, a proof is provided that shows the filtering convergence. The performance of the algorithm is evaluated via different operating scenarios of double line‐of‐sight measuring space surveillance. Simulation results demonstrate that the MMAE algorithm outperforms the robust filtering algorithms in the presence of the uncertainty and yields positioning accuracy similar to the extended Kalman filter in the absence of the uncertainty.