Abstract: Abstract：An parallelmodel adaptive estimation (PMAE) algorithm is presented for a timevarying system where  model uncertainty may occur occasionally. Generally, an algorithm which is designed for an uncertain system may yield suboptimal performance when the model uncertainty does not occur. To cope with this problem, we propose to use two filters in parallel in a multiplemodel framework. One of the filters, an augmented Kalman filter (AKF), provides estimates of uncertain parameters when the model uncertainties occur, whereas the second filter, a Kalman filter (KF), yields high precision in the absence of the uncertainties. A space surveillance example is given in simulation to show the potential application of the presented algorithm. It indicates that the PMAE is efficient to deal with model uncertainty.    

Key words: Keywords：multiplemodel adaptive estimation, uncertain system, augmented Kalman filter, space surveillance